IP-Enabled Energy Management
IP-Enabled Energy Management A Proven Strategy for Administering Energy as a Service Rob Aldrich John Parello
Acquisitions Editor: Agatha Kim Development Editor: Gary Schwartz Technical Editors: Dean Nelson, Tirth Ghose, and Matt Laherty Production Editor: Rachel Gigliotti Copy Editor: Sharon Wilkey Editorial Manager: Pete Gaughan Production Manager: Tim Tate Vice President and Executive Group Publisher: Richard Swadley Vice President and Publisher: Neil Edde Book Designers: Maureen Forys and Judy Fung Compositor: Jeff Wilson, Happenstance Type-O-Rama Proofreader: Publication Services, Inc. Indexer: Nancy Guenther Project Coordinator, Cover: Lynsey Stanford Cover Designer: Ryan Sneed Cover Image: Thomas Northcut / Digital Vision / Getty Images Copyright © 2010 by Wiley Publishing, Inc., Indianapolis, Indiana Published simultaneously in Canada ISBN: 978-0-470-60725-1 No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions. Limit of Liability/Disclaimer of Warranty: The publisher and the author make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation warranties of fitness for a particular purpose. No warranty may be created or extended by sales or promotional materials. The advice and strategies contained herein may not be suitable for every situation. This work is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If professional assistance is required, the services of a competent professional person should be sought. Neither the publisher nor the author shall be liable for damages arising herefrom. The fact that an organization or Web site is referred to in this work as a citation and/or a potential source of further information does not mean that the author or the publisher endorses the information the organization or Web site may provide or recommendations it may make. Further, readers should be aware that Internet Web sites listed in this work may have changed or disappeared between when this work was written and when it is read. For general information on our other products and services or to obtain technical support, please contact our Customer Care Department within the U.S. at (877) 762-2974, outside the U.S. at (317) 572-3993 or fax (317) 572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Library of Congress Cataloging-in-Publication Data Aldrich, Robert, 1973IP-enabled energy management : a proven strategy for administering energy as a service / Robert Aldrich, John Parello. p. cm. ISBN 978-0-470-60725-1 (pbk.) ISBN 978-0-470-94386-1 (ebk.) ISBN 978-0-470-94388-5 (ebk.) ISBN 978-0-470-94387-8 (ebk.) 1. Energy conservation. 2. Energy consumption--Automatic control. 3. Cisco Systems, Inc.--Automation. 4. Computer networks. I. Parello, John, 1966- II. Title. TJ163.26.A43 2010 658.2’6--dc22 2010031875 TRADEMARKS: Wiley, the Wiley logo, and the Sybex logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates, in the United States and other countries, and may not be used without written permission. All other trademarks are the property of their respective owners. Wiley Publishing, Inc., is not associated with any product or vendor mentioned in this book. 10 9 8 7 6 5 4 3 2 1
Dear Reader, Thank you for choosing IP-Enabled Energy Management: A Proven Strategy for Administering Energy as a Service. This book is part of a family of premium-quality Sybex books, all of which are written by outstanding authors who combine practical experience with a gift for teaching. Sybex was founded in 1976. More than 30 years later, we’re still committed to producing consistently exceptional books. With each of our titles, we’re working hard to set a new standard for the industry. From the paper we print on, to the authors we work with, our goal is to bring you the best books available. I hope you see all that reflected in these pages. I’d be very interested to hear your comments and get your feedback on how we’re doing. Feel free to let me know what you think about this or any other Sybex book by sending me an email at
[email protected]. If you think you’ve found a technical error in this book, please visit http://sybex.custhelp.com. Customer feedback is critical to our efforts at Sybex.
Best regards,
Neil Edde Vice President and Publisher Sybex, an Imprint of Wiley
This book is dedicated to my family for their unwavering support and especially to Kelly, Logan, and Piper for their inspiration. It is further dedicated to all those who recognize themselves ecologically as stewards of the greatest gift. —Rob Aldrich To my family for a lifetime of support and education and to Tirth Ghose for the professional partnership that created such a groundbreaking technology. Keep writing the music, my friend, and I’ll handle the lyrics. — John Parello
Acknowledgments We believe the writing of this book is an opportunity to architect and articulate a solution to a piece of a problem we are all just starting to understand—global resource management. There has been much discussion in the last four years on how technology professionals can apply existing tools to global problems related to the environment. If you combine the experience of the authors, this book represents nearly 10 years of dedicated focus in addition to a virtual team structure of nearly 80 people. We could not have written this book without the dedication, support, and professionalism of the virtual teams with which we interacted, primarily within Cisco. Creating a new technology at Cisco involved scores of people that provide their talents, time, and cooperation. It is impossible to name them all, but the entire Cisco family helped to create the work in this book and we thank you all. Our extended industry teams were made possible with the support of many of today’s leading technology companies. These companies all employ people who, like us, are trying to apply their knowledge of technology to the myriad of problems related to improving global, natural resource management. Given the scope and limited standardization that exists in energy management solutions today, not all companies with whom we interacted have specific solutions. However, all of these companies maintained an open posture relating to improved energy management, and each provided some piece of the puzzle. This posture, coupled with innovative thought across facilities and IT disciplines, gave us a body of work we believe should be shared. We have provided a table of the people and companies who, starting in 2006, have helped us shape the strategy addressed in this book in some way: Thanks to Company
Individual(s)
Contribution
American Power Conversion (APC)
Neil Rasmussen, John Tuccillo, Victor Avelar, and the entire APC Sciences Center
Providing many, many contributions to critical facilities modularity, IP management capabilities, and pushing the technological envelope for the data center facilities industry in general.
BB&T
Paul Marcoux
Providing wisdom of the ages across many elements of energy efficiency, from individual products all the way up through grid architectures.
Brunel University
Simon Furber
Telling us we had to build this, not for ourselves, but for customers like you. Thanks for being the lighthouse and the early deployment.
Cisco
Doug Alger
Making it a priority to facilitate shared learning on energy-efficient data center design.
Andy Broer
Providing leading capacity management services that account for environmental impacts.
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| Acknowledgments Thanks to Company
Individual(s)
Contribution
Cisco
Michael Brown
Providing years of professional support, friendship, and showing us how a writer is supposed to write.
Cisco Green Task Force
Providing countless hours of oddly timed, global-collaboration sessions and having a deep passion for affecting positive change.
Cisco Energy Management Services Team
Having an obsessive need to provide real solutions to real problems.
Cisco EnergyWise Engineering Team (Neetha Anandakumar, Richard Andrades, Amber Imam, Brock Miller, Jason Ristich, Ravi Rayi, Lucern Ma, Brad Schoening, Joe Salowey, Scott Fluhrer, and all the engineers, interns, and managers at Cisco in our cross company team that worked in and out of our code base)
Coding, testing, and working around every obstacle so quickly and so well. You are a great team and it is a pleasure to help you do the work you are good at. Thanks for joining in our little startup.
Cisco EnergyWise IETF Team Taking us “public” to the IETF with our MIB and the ongoing expert support. (Benoit Claise, Mouli Chandramouli, and the NSSTG team) Cisco EnergyWise Product, Marketing, and Program Management Teams (Sriram Balasubramaniam, Berna Devrim, Albert Mitchell, Ramesh Bijor, Siva Valliappan, Carmen Villalobos, Tom Zingale, and everyone in the product and marketing teams)
Providing the scope, development support, marketing, and logistics for a great product.
Cisco EnergyWise Test Team (Roger Abad, Isayas Abaye, Joe Guo, Dante Thompson, and all the testers and managers)
Testing our code and features even when we were early or late. “EnergyWise is working….”
Edna Conway
Providing strong leadership in addressing many elements of materials sourcing, supply chain management, and product compliance.
Piper Gianola
Driving a culture of considerate attention to the impacts of our activities.
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Acknowledgments ix
Thanks to Company
Individual(s)
Contribution
Cisco
Tirth Ghose
Creating Cisco EnergyWise. Thank you for Cisco EnergyWise. Your vision, partnership, and sheer technical skill made a new technology a reality.
Neil Harris
Providing creative thought and a thirst for pragmatic solutions.
Laura Ipsen
Providing leadership through the Cisco EcoBoard and Smart Grid teams.
Rik Irons-Mclean
Championing us in the UK and sweating it out with us for our first global demonstration.
Dave Katz
Implementing the first automated energy management service across multiple sites and proving big savings are possible.
Dave Kunkel
Providing resources, equipment, and “soundboarding” when we were frustrated and stuck.
Matthew Laherty
Being the very first business developer and product visionary for Cisco EnergyWise. Your broad depth in the energy management space, unlimited contacts, and drive to make a difference was the initial spark for the technology. You specified the problem that enabled us to build the solution and stood in for the big man when needed.
Mike Lavazza
Taking a leadership position in developing and implementing highly efficient data center designs.
Peter Linkin
Providing many years of support and an open mind to new solutions development.
John McCool
Offering leadership in product engineering through the Cisco EcoBoard.
Brock Miller
Choosing to join the Cisco EnergyWise team at its infancy and being the third in our group. Two people can play catch, but when you have the third, you have a team. Thanks for the hard work and amazing code.
Zeeshan Naseh
Providing vision along with business acumen to ensure services viability.
Scott Neumann and the Executive Marketing Team
Showing us what Cisco EnergyWise would be like in the future and the rock-star launch and demonstration.
Chris Noland
Implementing best practices, new technologies, and always pushing a Greener agenda.
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| Acknowledgments Thanks to Company
Individual(s)
Contribution
Cisco
Gnanaprakasam Pandian
Being the first executive sponsor for the development of Cisco EnergyWise. Thank you for the freedom to innovate.
Ron Ricci
Providing leadership in vision and strategy through the Cisco EcoBoard.
Dave Rogers
Keeping a consistent and steady focus on exposing the data.
Rob Rolfsen
Being the first to step up to the challenge of corporate sustainability and delivering a workable solution definition.
Andy Smith
Having determination and the willingness to jump into new technologies for the sake of a Greener world.
Darrel Stickler
Keeping focus on the data and providing a strong example of integrity to our team.
Luis Suau
Driving solutions development based on a foundation of actual deployments. You built the first Cisco EnergyWise mini-company in your lab to run it all.
Brad Schoening
Introducing John to Cisco, offering years of professional and personal friendship, and providing your help on Cisco EnergyWise. Vegas rules apply.
Don Schriner
Providing expertise in the energy management field and championing Cisco EnergyWise at its inception. You gave us the requirements that allowed us to innovate.
Rick Vroman
Providing support throughout the launch of Cisco Energy Management Services.
Cisco TechWise TV
Robb Boyd and Jimmy Ray Purser
Providing great video episodes and awesome tutorials. Thanks to you and your team for your world-class professionalism, craftsmanship, and vision.
Climate Savers Computing Initiative
All those involved
Pushing the technological and collaborative envelope of energy efficiency.
Cyclone Interactive
Earl Dimaculangan
Offering world-class information, architectural guidance, and programmatic development services.
Data Center Pulse
All those involved
Providing shared learning and real collaboration toward improving data center energy efficiency.
Dimension Data
Merle Singer and Steve Nola
Providing thought leadership on energyefficient design in systems integration and remote monitoring capabilities.
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Acknowledgments xi
Thanks to Company
Individual(s)
Contribution
eBay
Dean Nelson
Providing thought leadership and real change in the industry across data center and real estate operations.
EDS
Jeff Wacker
Demonstrating the need to incorporate sustainable planning into futures planning for large enterprises globally.
EMC
Dick Sullivan
Providing valuable insight on Information Lifecycle Management strategies and thought leadership related to energyefficient operations.
Emerson Network Power
Stephen Blakemore and Jack Pouchet
Providing multiple educational, planning, and design capabilities supporting energyefficient facilities design and operations.
Esty Environmental Partners
Daniel Esty
Writing Green to Gold, which, of course, was the first book we read that clearly articulates the many business cases for sustainability, and providing input on this book.
The Green Grid
All those involved
Delivering change through democratic innovation.
IBM
Jim Fletcher and the Tivoli Team
Providing the “10-minute” turnaround for an energy management dashboard in Tivoli.
Pacific Gas and Electric Company (PG&E)
Mark Bramfitt
Delivering thought leadership and financial incentives for improved energy management.
Quality Attributes Software
Craig Engelbrecht
Developing custom environmental monitoring dashboards and secure managed services.
SolarWinds
David Gardiner and the team at SolarWinds
Thanks to our partner with whom we had the most fun and for the rapid development of a management solution to show off our technology.
Sun Microsystems (2007–2009)
Mark Monroe
Providing many points of input related to data center and enterprise-level operative efficiency.
SynapSense
Jay Riley
Providing configuration and design support for sensor network deployments.
Syska Hennessy Group
Whitney Stone and Philip Curtis
Providing industry leadership and direct support for energy-efficient modeling and design services.
WTI
Daniel Morrison and Joseph Brodski and the team at WTI
Providing the PDUs and the rapid responses and the great support.
Yahoo!
Christina Page
Implementing new approaches, sharing lessons learned, and providing thought leadership in the IT industry.
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| Acknowledgments Enterprise-level energy management today is very much a collage of different technologies, operations, communications protocols, and case-specific kluges. The experiences we apply to the structure that is addressed in this book cross over many points of input, and they are far too many to mention here. However, we interact with many online communities and look forward to communicating with you and other readers of this book. We have covered these online forums, resources, and tools in the appendix at the end of the book. We would like to acknowledge the talent and integrity of the technical editors for this book. Tirth Ghose, Matthew Laherty, and Dean Nelson all provided insight above and beyond the normal duties of technical editing. Each of these editors is applying his technical knowledge to a greater good in some way, and for that we are lucky to be working with them. This book crosses over many technologies, organizational structures, and corporate strategies. We would like to acknowledge our employer, Cisco, for the flexibility and open posture in sharing internal lessons learned. This book is a reality because of the spirit of shared learning that Cisco maintains and the amazing people with whom we work. We also owe a big thanks to the leadership and employees of Cisco all across the globe! Finally, a special thanks to the Sybex team, whose patience, guidance, and professionalism were inspiring. Agatha Kim, our acquisitions editor, demonstrated an impressive talent, not just for understanding our technical frameworks, but also for steering us in the right direction on how best to deliver them in a broader context. Gary Schwartz, our development editor, was sagelike in his coaching, while Pete Gaughan, our managing editor, was very efficient and timely. Thanks in general to Sybex for all the incredible technical materials over the years—a smarter world is a happier world, after all.
About the Authors Both authors are currently employed at Cisco, focusing on energy management technologies. Their combined 30 years plus of energy management knowledge is applied in this book. Although their backgrounds are different, they are both focused personally and professionally on developing better solutions to global challenges related to the environment. Rob and John have been working together extensively since late 2007. Rob Aldrich is a principal solutions architect and energy-efficiency expert at Cisco. His role involves working with Cisco customers, Cisco IT and Labs, Cisco Central Development Office product developers, and Workplace Resources divisions. His background prior to joining Cisco in 2005 was in power and cooling design for enterprise-class data centers. He has held positions in both facilities and IT operations, including product management, systems engineering, technical marketing, and field services. In addition to practical experience in facilities and IT operations, design, deployment, and management, his degree is in environmental sciences. This mix of backgrounds provides a unique knowledge base across energy, pollution, carbon regulatory frameworks, data center design, and corporate processes. At Cisco, he has applied structure to this knowledge through programmatic frameworks such as the Data Center Assurance and Efficiency Assurance Programs. Rob has been directly involved in the development of Cisco EnergyWise and more recently led the establishment of Cisco’s Facilities and Energy Management Services Practice. In his current role, Rob leads this team in helping IT and facilities users set the foundation to approach IP-enabled energy management enterprise-wide. Rob grew up in a military family and has lived in several regions of the world. Born in Newport, Rhode Island, and then moving on to Puerto Rico, Saudi Arabia, Australia, and several U.S. cities including Boston, New York, San Jose, and San Francisco, Rob now resides in Portland, Oregon. Rob is an avid foodie and outdoor enthusiast who enjoys history, graphic arts, martial arts, playing basketball, mountain biking, and all things aquatic. Rob’s personal goal is to make the impacts of energy usage highly visible so consumers will be empowered to make ecologically sound choices in how they plan, design, and use IT systems. John Parello is currently a technical leader in the Ethernet Switching and Technology Group at Cisco. He is the lead architect and coinventor of Cisco EnergyWise, which is a cornerstone of Cisco’s Green initiatives. John has championed the design and development of Cisco EnergyWise and its cloud-based architecture. He has been educating and presenting the principles of IP-based energy management within Cisco, throughout the industry, and to standards bodies. John joined Cisco in 1999 as an engineer in the Network Management Technology Group. He was responsible for developing network management applications focusing on fault processing and event correlation for campus and telephony management applications. He holds three patents in that area and has also focused on network management appliances based on Linux. John later moved to the Wireless Network Technology Group at
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| about the authors Cisco, where he worked on the Wireless LAN Solution Engine and was the lead developer for the wireless self-healing features. Before joining Cisco, John was the head of engineering for Avesta Technologies, a network management start-up specializing in fault and performance correlation. Prior to Avesta, he worked for Morgan Stanley, specializing in high-volume cash transaction processing and predictions. John began his career in New York, working for the public utility, Brooklyn Union Gas Company, where he worked on large-scale, object-oriented systems and databases. John’s career in software engineering spans more than 25 years. He attended New York University and received a bachelor’s degree in computer science. While still a student, he developed the university’s first production computerized registration system. He holds a master’s degree in electrical engineering and computer science from Stevens Institute of Technology and was an adjunct professor of computer science at Pace University. John was born and raised in New York City and now resides in San Jose, California. He is an avid soccer player and fan and likes to combine his interests in soccer and travel by chasing World Cup games around the globe.
Contents at a Glance Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi Chapter 1 • A Stake in the Ground . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Chapter 2 • Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Chapter 3 • Assessing Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Chapter 4 • Managing Your Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Chapter 5 • Building a Pilot Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Chapter 6 • Pilot to Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Chapter 7 • Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Chapter 8 • Administering Energy Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Chapter 9 • Making Your Program Sustainable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Chapter 10 • Preparing for the Next Big Thing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Appendix A • The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Appendix B • Links and Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi
Chapter 1 • A Stake in the Ground . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 How Did We Get Here? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Why Should You Care? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Collaborate and Conquer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 What You Should Know about Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Where Does It Come From? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 How Is It Used? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Energy Accounting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Calculating Your Energy Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Energy Intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Cost Allocations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Energy Use in the Digital Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 How Is It Being Used Today? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Emerging Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 The State of Energy Management Today . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Where Is Energy Management Technology Adoption Today? . . . . . . . . . . . . . . . . . . . 26 Energy Sourcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Renewable Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 The Future of Energy Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Smart Loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Where Is Energy Management Headed? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Chapter 2 • Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Understanding the Scope of Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Understanding Traditional Accounting Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . Use What Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Accessing Benchmark Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Getting Permission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Where to Get It . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Instrumentation Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structuring the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Program Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Benchmark Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
43 44 45 50 51 56 61 64 64 71 74
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| Contents Chapter 3 • Assessing Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Organizing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Finding a Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ensuring Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prioritizing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Translating Data Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Formulaic Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qualitative Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Presenting the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparative Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sharing Vision and Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
75 76 77 79 83 83 84 86 86 89 93 96
Chapter 4 • Managing Your Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Drafting a Project Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Building a Team . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Virtual Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 Aggregating Resources and Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Project Milestones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Getting Organized . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Getting Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Structuring Your Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Chapter 5 • Building a Pilot Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Understanding Energy Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FCAPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FCAPS + E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selecting Your Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Defining the Mission and Philosophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Creating the Root System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Determining Hardware Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Setting Up the Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Choosing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gathering the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Understanding Energy Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Natural Domain Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy Domains as Smart Loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selecting Pilot Energy Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Communicating Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
117 118 118 119 120 122 122 123 126 133 136 136 137 139 141 142 143
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Chapter 6 • Pilot to Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Creating a Production Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reviewing the Pilot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Performing Inventory and Categorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Audit Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roles, Ratings, and Tags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monitored Data and Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implementing Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Passive and Manual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Active . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
145 147 147 149 150 151 155 156 157 157 158
Chapter 7 • Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Information Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Government Mandates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . U.S. Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . European Union Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chinese Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emerging Nations and South African Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effectiveness of Government-Mandated Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . Conversion to GHG and CO2 Equivalencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy Domain Breakdown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Live, Operational, and Historical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
159 160 160 161 161 162 162 163 164 166 170
Chapter 8 • Administering Energy Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Organizing the Energy Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Performing Physical Energy Domain Administration . . . . . . . . . . . . . . . . . . . . . . . . Classifying Energy Consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Applying Classifications to Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Specifying Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Static Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dynamic Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enforced Versus Suggested Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implementing Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
171 174 176 178 179 179 180 180 182 184
Chapter 9 • Making Your Program Sustainable . . . . . . . . . . . . . . . . . . . . . . . . 185 Funding Your Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Programmatic Funding Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Determining Program Placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IT Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Facilities and Real Estate Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
185 186 188 189 191
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| Contents Choosing a Program Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Centralize It . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribute It . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pay for It . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Program Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vision and Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Executive Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
192 192 193 194 194 198 199 200 201
Chapter 10 • Preparing for the Next Big Thing . . . . . . . . . . . . . . . . . . . . . . . . . 203 Chart Your Course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emerging Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Water Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cloud Computing and Energy Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Center Energy Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . On the Horizon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Making Energy Usage Visible . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Using External Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Resource Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tiering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Defining Workloads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bringing Power to the Packet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
203 204 205 210 212 215 216 217 219 219 220 220 221
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Appendix A • The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Chapter 2: Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 3: Assessing Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 4: Managing Your Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 5: Building a Pilot Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 6: Pilot to Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 7: Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 8: Administering Energy Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 9: Making Your Program Sustainable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 10: Preparing for the Next Big Thing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
225 226 227 228 229 231 232 233 233
Appendix B • Links and Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Useful Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Topical Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research, Analysis, and Educational Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
235 238 238 239
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Introduction We believe the book you are holding is an industry first. Both authors have been hungrily searching for an actionable technology plan for a few years. Although there are some great reads out there that address the business elements and theory behind Green technologies, we haven’t seen anything yet that references a specific deployment. This book was born out of a desire for real change in how we use natural resources today for digital purposes. We believe the time for talk has passed, and it is time to take action. It is time for the IT industry to clean up its own backyard and, in doing so, build the tools the rest of the world needs to better manage global climate change. When you ask yourself, What can I do? How long do you spend on the question? When you look at the sheer size and complexity of this question in the context of global climate change, resource security, and the popular notions of Green, it can lead to analysis paralysis. We were lucky to enough to get paid to answer this question starting back in 2005. It took our small core team and a large virtual team close to four years to answer it, but we did, and we strongly believe that this answer is extensible to others. Given the range of technologies we address in this book, we are not trying to sell a product or service here; we are simply looking to share a strategy and methodology that is working well for us. We encourage our readers to take what works, to leave what doesn’t, and to share your lessons with your respective communities of technology professionals. As we look across the loosely defined Green technology industry today, we see many point solutions but few strategies and methodologies for applying them in an enterprise context. We believe this industry needs more prescription and less theory in the solutions that are being offered to be Greener. We believe this so strongly that we decided to write this book as proof that you can apply existing technologies to your organization’s energy consumption and greenhouse gas emissions and have a major impact. It is, of course, risky to be the first to set sail, but so far the seas have been calm through our first 1.5 years of our energy management services development. To put into perspective the approach we outline here, some analogies are needed. Think of today’s IT industry as the transportation industry. How did the modern transportation era start? It started with the locomotive, of course. Now think of these early locomotives as the first generation of mainframes. Although not completely gone, mainframes have given way to volume servers (cars). Now volume servers are becoming more efficient, and the newer blade and unified computing systems start to look like hybrid cars. Although this is a loose analogy, the point to take away is that companies build solutions in response to consumer demand. If we all demand better performance at a lower price point but forget about energy efficiency as a primary feature, we will not see the changes we need across the IT industry anytime soon. As Henry Ford famously said, “If I’d asked my customers what they wanted, they’d have said a faster horse.” This book will arm you with the tools and lessons you need to be, at worst, a more informed consumer and, at best, to replicate our successes in better managing energy.
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| Introduction What This Book Covers This book is primarily about three things: •u Assessing problems •u Assessing solutions •u Implementing a strategy to improve the energy efficiency of your business operations
through IP-enabled energy management That is the what. The how, first introduced in this book, is that we cover ways to improve energy efficiency by simply shutting things off when not in use. Getting deeper into the how, you will see that we are doing this by using established technologies enabled by the Internet Protocol (IP). Finally comes the why. Why is this book different from many other books available on energy efficiency? It is different because it is using the scale of IP networks globally to “govern” the energy use of an ever-widening range of IP-enabled products—analogous to desktop shutdown software for any asset attached to the World Wide Web. We tried to cover what is available today, and we’ve intentionally limited the theoretical discourse of being Green. We are focused on enabling better management of the electrical consumption of the many billions of computer-like assets deployed across the globe. As a secondary focus, we cover the impacts of improved energy management for computer systems on building infrastructure. In terms of what is available today, we go through how we’ve been able to implement current-state technologies within a large enterprise to save on energy and costs. Throughout the book, you will find many practical notes and tips to help you, not just to understand what is available today, but also to know whether it’s right for your operations. Given the scope and complexity of energy usage, climate change, and information technology (IT) operations, this book took almost two years to write. Just the scoping of the content took 10 months. With that in mind, we’ve tried to walk the line between too much and too little. The technical content is not so deep that it will lose business planners and vice versa. Our hope is that this book will help our peers and our peers-to-be to build a programmatic framework to manage energy better as an IT service. This book is directly tied to our work at Cisco Systems, but covers many adjacent technologies that we’ve employed in our energy management services creation. This book’s only specific point of advocacy is to better manage energy use related to IT operations. To that end, our aim is to give you the tools you need to determine the best approach for you.
The History and Drivers Energy management does not have a long history. In order to get your head around enterprise and even country-level energy management, some context is needed. Going back to the discovery of electricity, very few barriers have existed to generating more and more of it globally. Its usage is directly tied to all kinds of economic and quality-of-life indexes. In so many ways, we rely on it for our very survival, and yet we treat it as an infinite resource. Many don’t even make the connection between petroleum-based fuels and electricity. Fewer still realize the greenhouse gas (GHG) emissions related to electrical usage are double that of transportation. In a global setting, the first connection we help you make is that using electricity for anything today is a dirty and costly activity. The second is that computer-based products use a lot of electricity. You will see more on this in later chapters, where we show the example of the IT industry contributing as much GHG as the entire airline industry. The last connection is that
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Introduction xxiii
the way we use computer-based equipment today is for the most part in an “always-on” default state. With this as the setting, we take you through the programmatic framework and technology needed to start changing this state. After you make these connections and then realize that the resources are not infinite, you will notice that energy management becomes not only necessary but also a new field of study. So what are the drivers for the technology of energy management? One of the things to look at is the convergence of technologies sparked by the advent of the Internet. Just as telephony networks have merged with the information network, the power grid will be merging with the data network. This convergence is driving energy management as a field of interest. The good thing for energy management is that, as different networks converge and combine, the best practices and lessons learned are incorporated into the merged network. The lessons learned from mainframe systems management, telephony management, PC management, and building management all combine. So now, with the new field of energy management, we are ready to leverage all of that experience. This book attempts to show how you can justify, fund, and implement an energy management system using the historical base, current driving forces, and experience from converging IT systems management.
Why Is It Unique? What makes this book unique is that we bring together two distinct personal and professional histories. Rob Aldrich has a long history with resource management, ecology, and data center management. John Parello brings experience from public utilities, network management, and high-speed/high-volume data management. At first glance, these experiences may seem unrelated, but these are all features that energy management systems will need. Both authors have been working at Cisco Systems and have witnessed and worked firsthand on the convergence of similar systems with the Internet. We all know how our lives change as we go online, and more of our day-to-day activities are network based. Although this is true for individuals, the average person does not see when businesses and systems become network based. When this happens, paradigm shifts create waves of change in their respective industries. Think of how the financial industry, manufacturing, entertainment, and even food services have all changed because of networking. As industries become network based, waves of change are created. We have successfully navigated these changes. With this book, we bring together those experiences to show you how to navigate the changes taking place in energy management.
What Went into It? This book describes our firsthand experience of taking what we knew from other management areas and applying it to a pilot energy management program. When we first decided to create an energy management solution, we thought about it with a traditional approach—buy some servers, get some software, and manage it. What we quickly realized was that this cannot scale. Ideally, with energy management, you would want to manage and track everything that draws power. The number of things to manage is enormous. We drew on our experience with managing networks, telephony, wireless, and data centers and realized that we needed a solution that grew with the number of things we wanted to manage. This resulted in a new technology, the first IP-based energy management solution built
xxiv
| Introduction directly into the network. This technology has set the foundation for real-time energy management and control within the network and is managed using a cloud-based model. The name we gave to this technology is Cisco EnergyWise. With this new technology, we also created new methodologies and new criteria for socialization that you can use to create and execute your own plans.
What Will You Get out of It? We hope that when you are finished with this book, you will have a better understanding of how electricity generation, distribution, storage, consumption, and GHG emissions apply to the end consumer. We want you to come away with an understanding of how to create a business case for implementing a new energy management program that can result in a new IT service. We hope to show you the changes that are coming and how energy management will be a significant field of interest and study. We took a journey into energy management and, as you get ready to embark on one yourself, you’ll get to hear our experiences in order to make your experiences more successful.
Who Should Buy This Book? We make certain assumptions regarding the reader: •u You are familiar with basic computing functions, applications, tools, organizational struc-
tures, and corporate processes. •u You have an interest in applying technology toward improved resource management. •u You have used and are comfortable with spreadsheets, databases, and presentation tools. •u You are familiar with basic network management or building management systems.
This book is not intended as a “one-size-fits-all” guide to deploying a single energy management technology. The technology available today can be brought together in a meaningful way, but at the time of this writing, there are no one-stop shops available. This book represents the best of what we’ve seen that is available today. This book should serve as a good introduction to IP-enabled energy management, and we will highlight why this approach has the best potential for “Greening” the IT industry today. Even if you’re not in a senior position within the IT or facilities industry, this book will prepare you for a senior-level role focused on energy management services. We believe that many secondary education efforts would benefit from the tenets of this work, and we’ve tried to structure its readability to meet the passion we see in colleagues all over the world today. If you want to make a difference and have a passion for information technologies and energy usage, this book is for you.
What’s Inside This Book? Here is a glance at what’s in each chapter: Chapter 1: A Stake in the Ground begins by describing energy management—the relationship to help improved natural resource management, the global economics, and the current state of energy management technologies.
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Chapter 2: Benchmarking provides a structured methodology covering data modeling for cost allocations, capacity analysis, and departmental billing structures. Chapter 3: Assessing Value begins with an approach focused on the iterative improvement of energy-related data—the analytic models, normalization methods, and documentation examples. Chapter 4: Managing Your Project covers the logistical requirements and team planning needed to prepare for a pilot implementation—the human resource requirements, workload management, and project cadence. Chapter 5: Building a Pilot Deployment covers how to select a team and implement a pilot energy management system based on experience gained from successful projects. Chapter 6: Pilot to Production covers how to expand a pilot energy management system to a production system by describing the information you need to track and how to get it. Chapter 7: Reporting covers how to report information from a pilot or production system for stakeholders and potential regulatory bodies. Chapter 8: Administering Energy Domains covers how to partition and manage a deployment in reasonable sizes. Chapter 9: Making Your Program Sustainable brings us to a point of long-term program planning and development. Funding models, expansion strategy, and economic-ecologic scalability are all addressed. Chapter 10: Preparing for the Next Big Thing forges ahead into uncharted territories and provides plausible scenarios across IT and facilities technologies, civic and corporate energy reporting, and business priorities.
How to Contact the Authors We welcome feedback from you about this book or about books you’d like to see from us in the future. You can reach the lead author, Rob Aldrich, by visiting www.robaldrich.com. John Parello, the contributing author, can be reached by visiting www.johnparello.com. Both authors contribute to the site www.ipenergyservices.com. This IP Energy Management Services website has been set up as a specific resource for this book and provides a blog, tools, and insights to complement the content of this book. Sybex strives to keep you supplied with the latest tools and information you need for your work. Please check their website at www.sybex.com, where we’ll post additional content and updates that supplement this book should the need arise. Enter this book’s title in the Search box (or type this book’s ISBN: 978-0-470-60725-1), and click Go to get to the book’s update page.
Chapter 1
A Stake in the Ground When thinking about energy management, you have to start somewhere. For the sake of this book, start by thinking about energy in terms of risk, cost, and resource management. Then look at energy management in terms of our global dependence on energy in all its commercially available forms. With this mindset, you can begin to walk the line between facilities and IT management by strategically applying emerging energy management technologies. As you assess the current state of energy management within your organization, you will find there is much room for improvement and greater risk than you realize. This chapter focuses on the foundational elements you need to know to build an energy management program. Having this program in place has many benefits, including improving the level of control over your organization’s future as it relates to energy use. For most of us living in industrialized nations, energy in the form of electricity has largely been taken for granted. It’s been ample, reliable, and cheap. In fact, given the direct correlation between energy use and gross domestic product (GDP), using less energy goes against the very principles of economic progress. Using more energy is inextricably linked to progress, prosperity, and growth in the way our global economy operates today. For these reasons, a purely marketing-driven Green program will not help a large organization to build more-sustainable operations in the long term. Programs that rely too much on a use less mentality and not enough on sound financial planning are destined to fail. If your organization already has a Green focus, what you should be asking is, How sustainable is your sustainability program? Without executive and functional business alignment and the human resources needed to effect change, the answer to the question is not very. Reducing costs through improved resource management can set an organization on the path to being more sustainable and profitable, while reducing mid- to long-term risk along the way. This book focuses on what is actionable: cost reductions through improved resource management. The construct of the problem doesn’t need to be more complex than that. Your solution can indeed be this simple when you approach this book as a guide to applying information technology toward improved resource management. To that end, this chapter provides a good foundation for approaching resource management focused on energy in much the same way we manage anything else in a large organization. In this chapter, you will learn the following: •u How to develop critical thinking in applying technology to improve resource management •u How to acquire a high-level understanding of global energy economics •u How to assess the current scope of technologies focused on energy management
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How Did We Get Here? In the coming decades, managing energy use with information technology tools will be as commonplace as industrial automation. It’s fairly obvious that climate change, market volatility, finite resources, and many social drivers will thrust us into a new era of automated management of natural resources. How that will happen and what technologies will be used are considerations that are closer to being answered than you might think. However, we have a lot of ground to cover to make up for 100 years of unbridled industrialization with few thoughts of constraint. To date, we have relied on fairly antiquated methods of gathering and analyzing data related to global resource consumption. Even the most precise estimates of residential, commercial, and industrial energy usage rely on instrumentation and networking technology that is essentially 50 years old. For example, the supervisory control and data acquisition (SCADA) networks that are used today to manage our electrical grids have not changed much since the 1960s. This is so because we have yet to see the need to broadly instrument the many things we make that use energy. We have fuel gauges in our cars that tell us when to fill the tank, but that information is not networked and analyzed. We have monthly utility bills that tell us roughly how much gas and electricity we’ve used. However, we have not gotten to the point of networking energyconsuming products and activities en masse. Networking our consumption, of course, can’t be accomplished until we instrument the components we want to network. Until very recently, this has been cost prohibitive and administratively intensive. In addition to the most basic instrumentation challenges, the drivers to measure our consumption have not existed. Data centers, the most energy-intensive environments in IT, have only recently been experiencing capacity shortfalls after 30 years of unabated growth. In addition to reliable capacity, reliable quality has also been the norm—at least for most of the developed world. However, both of these assumed attributes of electrical supply have been challenged in recent years. The United States, most of Western Europe, Japan, South Africa, India, and China have all experienced a high degree of volatility in their energy markets for some time now. For IT operations in these countries, capacity constraints and increased utility prices have been major drivers for consolidation projects. Although there are daily challenges in procuring and maintaining a reliable electrical supply, corporations in the United States, Europe, Japan, and China still operate under good power conditions. No immediate operative pressure exists for wholesale change. India’s supply is more challenging, but still many data and call centers in this country compete globally with little major disruption related to power. The reality is that for any large organization seeking growth or looking to save costs, energy management will be a necessary and strategic focus area. What we are seeing is the beginning of the end of inexpensive electricity. To put things into perspective, look at IT today. IT operations across the globe are just starting to pay attention to power. The need is recognized, but the technology and cost incentives are not clearly understood. Large organizations do typically try to forecast energy capacity requirements, consider power costs in business-siting decisions, and negotiate with utility providers, but they do not do much more. Some progressive private-sector corporations have instituted laudable efficiency programs, but for the masses, energy has been something IT departments think about only when they start to run out of it. Figure 1.1 illustrates the growth in carbon dioxide emissions (y-axis = millions of metric tons of CO2) resulting from electrical use in IT from 1990 to 2008.
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How Did We Get Here? 3
Index of Total Industrial Output and Selected Industries (2002 = 100)
Figure 1.1 In the past 20 years, there has been a meteoric rise in electrical use related to the IT industry. U.S. Energy Information Administration, 2008 (www.eia.doe.gov/ oiaf/1605/flash/ flash.html)
300 Compters and Semiconductors
250
Primary Metals
200
Petroleum Refiners 150 100 50
Total Industrial 2008
2006
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0
A passive stance on IT’s energy use may continue to be a low-risk policy for many organizations. Many facilities-oriented professionals have been conditioned over the years (myself included to some extent) to take a wait-and-see approach. Reliable energy supply is, after all, the most critical service of your business. The fact that facilities professionals are the ones who get the 2 a.m. phone calls first and are equally as often the first to be fired is a contributing factor in the difficulties IT and Facilities departments have in working together to manage energy. However, new technologies are now coming to market that are game changers. You can be at the crest of this wave, but knowing the technological, organizational, and financial history is important.
Greenbacks The apparent divide between public opinion and real monetary investment in energy management is narrowing quickly. As of 2009, the combination of clean technology and Green technology accounted for the single largest investment area for venture capital firms in Silicon Valley, California. On October 27, 2009, President Obama injected $3.4 billion to fund Internet Protocol (IP) enablement of the U.S. electrical grid.
Why Should You Care? Think of how we use energy today. Let’s start with a simple analogy using a data center and a home. Assume you are about to leave on a weeklong summer trip. Just before heading to the airport, you take 10 minutes to turn on every appliance in your home—you set the air conditioning to 60° F while all the windows are open, you turn on the shower and all the other faucets in
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your home to their hottest settings, and finally, you turn on your kitchen range top and oven as well as the microwave and all the lights in your house. This is similar to how IT is run today. In data center environments, basic thermostats are the norm. Yes, data center cooling systems use the same basic environmental control technology as any newer home. When you recognize just how much we waste by not including better instrumentation and environmental controls and you see the economic and environmental costs that result, you’ll have that ah-ha moment. The executives in your organization are paying more attention to energy and CO2 emissions now than at any point in their careers. They are hungry for solutions, and most see technology as the answer. Whether you are a real estate, facilities, or IT professional, you have a better chance now of making an impact through energy management than ever before. You can be the first in your organization to challenge the current expectation of energy management, as shown in Figure 1.2. You can also be the first to demonstrate how technology can provide dramatic benefits related to cost, risk, and the environment. Because the growth in energy use is commonly most acute for IT departments within a company, the issue becomes even more critical in IT-intense companies. Thus, if technology use is causing the problem, surely technology can provide a solution. The reality, of course, is that the need for a solution depends on the priority being placed on energy-related issues and how they are being managed. Many organizations move based only on cost savings and risk mitigation. With this in mind, you should look at some key areas that provide insight into energy management risk and cost factors. What will very likely be the case is the following: •u You are operating as much as 90 percent of the infrastructure that supports your IT opera-
tion 8,760 hours per year. •u Your computer asset utilization is below 20 percent. •u Your storage asset utilization is below 40 percent. •u You are overcooling your data centers. •u You use lower-efficiency distribution voltages. •u You are using more physical space than you need. •u You have not networked your IT energy use. •u You have not networked building energy use. •u You have no active thermal management.
Your biggest initial challenge in getting the attention and support of your peers and management might be basic communications and structure. You will need to share your ah-ha moment with passion but based on a strong business foundation. Your message on the value of energy management could be diluted if you tilt too far toward Green labeling. Focusing too much on cost savings without first having a cost-sharing model agreed upon by Facilities and IT may also be a nonstarter. What should be effective is your building the ability to articulate shared values clearly across multiple stakeholder groups. Basically, your value matrix will address energy costs, Green branding, capacity planning, and, if implemented properly, risk.
IMPACT
Suresh Balakrishnan and Donald Spicer, ECAR Research Bulletin, Issue 20 : “Climate Change, Campus Commitments, and IT,” 2009
Energy management is now at a point where it can be moved into the Low Difficulty, High Impact quadrant.
Figure 1.2
Power Management of PCs
LOW DIFFICULTY, LOW IMPACT
Electronic Reserves
Buy Biodegradable Materials
Lights Off in Data Centers
Proper Disposal Procedures
Reduce Hardcopy Reports
Online Collaborative Communications
Phase Out CRT Monitors Engage Students to Promote Cultural Change
Print Management for Students
Extend Refresh Cycle
Recycled Paper
Purchase Energy Star Qualified Computers/Equipment
RFP Policy
Energy Management/ Monitoring System
Reconfigure Data Center
Remote Management of CPUs
Replace Old HVAC Efficient Cooling
Virtual Labs
Thin Clients
Print Management for Faculty/Staff
Turn Off Power Strips
Intranet for Document Collaboration
HIGH DIFFICULTY, LOW IMPACT
Unified Messaging
Education/Awareness
Replace Desktops with Laptops
Videoconferencing from Laptops
Outsource Student E-Mail
Teleworking
Online Business Processes/ Academic Programs
HIGH DIFFICULTY, HIGH IMPACT
Digital Imaging
Enhance Videoconferencing
Consolidation/ Virtualization of Servers
Analysis of IT Sustainability Initiatives LOW DIFFICULTY, HIGH IMPACT
How Did We Get Here? 5
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By its very nature, an inflection point happens through the acquisition of information. In building your role as an energy manager, you need to be able to provide in-depth consultation on energy costs, risks, and greenhouse gas (GHG) management in a context your stakeholders can understand. Because there are many stakeholders with whom you will interact, this chapter focuses on the foundational elements of what you need to build upon. There is no blueprint for influencing others to hold the same viewpoint as you do, but professional empathy and research go a long way. The technology is here and much simpler than ever to deploy. It is up to you to build your understanding of the resource this technology will manage—electricity.
Collaborate and Conquer If your organization already has a Green initiative underway, you should reach out to this group. Joining forces with any existing program can provide visibility (and in some cases resources) to any program you look to form around energy management. You will likely be welcomed with open arms by any current Green programs, because many are structured and delivered from within a corporate social responsibility (CSR) function. These CSR functions often lack the technological background and organizational support to take advantage of the tools you could administer. If your organization does not have an existing Green program or CSR function, you might take on that role. Public opinion is also on your side, as shown in Figure 1.3.
Figure 1.3
GLOBAL ENERGY CONCERNS
Public opinion polls show that concerns related to security, energy, and the environment are growing globally.
Concern about harm to environment
Concern competition will lead to conflict Brazil Canada France
GlobeScan, Program in International Policy Attitudes (PIPA)
Germany India Italy Kenya Russia UK US 100
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Figure shows % “very” and “somewhat” concerned Polling conducted: 26 May to 2 July 2006 Sample size: 19,579 people in 19 countries Margin of error +/–2.5 to 4%
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How Did We Get Here? 7
Even with the fear, uncertainty, and doubt that blackouts and melting glaciers bring, little wholesale action has been taken to date. Why is this the case? Much of it is technology related, some is habitual, and some just stems from legacy accounting systems. In our experience, the hierarchy of priorities and how they are presented are major factors as well. It’s usually a combination of all of the preceding factors if you work for a large organization. These are important factors to remember, to manage your own frustration levels as you work through the complex mess of setting up energy management for the first time. Understanding the history of energy markets, consumer behavior, and emerging technologies will give you a reasonable chance at making big changes in your organization. Your knowledge of the economics of energy and the technology available to manage it will be your currency as you engage other teams. The reality is that in a collaborative virtual team model, you need to give in order to get. Matching your skill sets up with others in the organization who have skills in core and adjacent fields will improve your chances of success. In addition to the knowledge-sharing aspects of a virtual team model, you can also help with some very basic things such as file sharing, storage, video conferencing, wikis, and so on. For many, the collaboration tools we in IT take for granted can be new productivity multipliers for others. Don’t underestimate the value of these tools and, at the same time, don’t overestimate people’s awareness and understanding of them. Carve some time out to get hands-on with those individuals who are key to your success. Finally, you will find that there are many tools in the facilities industry that can provide significant value. Building Information Modeling (BIM), Building Management Systems (BMS), and customizable databases such as OSIsoft are increasingly becoming power tools. These systems can provide significant insight into facilities data points (power, cooling, utility rates, departmental billing, and so forth). Access to these tools and the data they provide may be all you need to establish benchmarks across a range of business operations. Getting access to these systems or working with somebody who does will save you time in mining data and allocating energy costs to assets and operations. We discuss more on establishing benchmarks using this data in Chapter 2, “Benchmarking.”
Green Programs Your organization might have some form of a sustainability program already. As an exercise, look into the structure of this program and ask some probing questions about the program’s structure. Does the program have a budget and dedicated head count, or is it a voluntary effort relying on a “tin cup” for funding? Has it mapped any Green goals to existing or proposed business priorities such as data center consolidation or supply-chain management? In general, is it a sustainable program with specific deliverables that will appeal to people who don’t believe that climate change and environmental management are priorities? If the answer is no to some or all of these questions, the program probably lacks centralized management of the issues. This is not surprising when you consider the very nature of something as complex and fragmented as Green. However, if a program has the ability to garner savings and strategically reinvest them, certain elements of a Green agenda can be supported through an iterative approach. The prioritization of those elements will be a balance between economic and ecologic viability. Said another way, there is no secret playbook for forming a Green program, but if it’s a part of a business, then it will be run like one.
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What You Should Know about Energy From the climate crisis to armed conflict and poverty, many of the world’s most pressing challenges are connected to our rising—and often unsustainable—demand for energy. These interdependencies must be understood, and they must be addressed. Ban Ki-moon You don’t need to be an expert on the topic of energy in order to implement a project to manage it. That is, treat energy like any other service you would manage in an IT operation. Think of it like Voice over Internet Protocol (VoIP), a simple network-based service that has quality of service (QoS) expectations and service-oriented architectures (SOAs). You can find individuals to fill electrical knowledge gaps, but at the very least you need to do some self-study to have a basic understanding of what you are managing. This section discusses how you can build a foundation of understanding regarding global electrical generation and consumption. You need to know the basics of energy economics to be credible if you are to position yourself as an energy manager. This section also provides a global energy review in terms of a source, fate, and transfer methodology. This methodology is also used in pollution management, as it can be applied to a wide variety of geographies and use cases. Put another way, we provide insight into how electricity is generated (source), how it is used (fate), and how it is transferred (managed). This topdown framework provides a conceptual management structure that you can build upon. After you have a conceptual framework in mind, you will need to build a knowledge base. To get started, spend some time reviewing the historical data publicly available on these sites: •u United States: www.eia.doe.gov •u Europe: www.energy.eu •u Japan: www.ieej.or.jp
These will be good reference sites for you when you are modeling deployment results from region to region. Be sure to bookmark them. Other good sites are out there as a result of the European Codes of Conduct for ICT, starting with the European Commission’s Joint Research Centre Institute for Energy site at http://re.jrc.ec.europa.eu/energyefficiency. Some private-sector ones are also available, such as www.openeco.org. OpenEco was originally backed by Sun Microsystems, and it serves as a good ongoing reference in terms of buildings’ energy use. It also might provide a glimpse of things to come. Some laudable social-networkingstyle sites have sprung up as well. One to watch is http://datacenterpulse.org, where data center managers network to drive energy efficiency into their operations. Started in 2009, it is up to 1,400 members in 55 countries. The managers who network on this site collectively represent billions of dollars in annual IT expenditures. The site’s popularity proves there is an appetite for more development in this area. Continuous self-study in this area is necessary to keep up with new developments and to build your program further. In general, focus more on the economics of energy than the physics. Be sure to keep risk at the forefront of your mind, because energy in the form of electricity is the most critical service for any business. As your approach to energy management matures, you
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What You Should Know about Energy 9
will need to dive deeper into the physics of electrical efficiency, creation, transmission, and storage. But for now, implementing energy management within a 3–5 percent margin of error with the intent of saving money and reducing risk will suffice. The last high-level point to consider is to beware of analysis paralysis. Said another way, we don’t limit our electrical consumption related to IT in any meaningful way today, so don’t let the perfect be the enemy of the good.
Where Does It Come From? A lot of debate occurs on how we source our energy. This book does not delve into the pros and cons for the types of fuels we use to generate electricity. Al Gore’s Our Choice: A Plan to Save the Climate Crisis (Rodale Books, 2009) is a must-read if your interest lies there. Instead, in this book, we have generally opted for a politically neutral stance and a corporate context, and we have focused primarily on energy costs. However, in our careers, we have often been put in the position of offering an opinion on what types of power are available. To that end, we’ve provided a short section to give you a basic economic perspective on electrical generation. The sun, to be sure, is the primary fuel source in our biosphere. Although some tubeworms are doing well with geothermal energy, almost all of our energy starts with the sun. So if this star is the source of energy, we can say that the transfer varies widely, and the fate is relatively predictable in human terms. Building occupancy, transportation, manufacturing, conflict, and food consumption are the activities we measure most effectively today. The vast majority of the energy that goes into these activities starts with the sun. As for oil and coal, they came from the organic remains of flora and fauna that acquired their energy from the sun. Wind energy comes from the convection created by the sun warming the oceans and land. When you eat a vegetable or fruit, you are rearranging and utilizing photons from the sun. So it would have made perfect sense if we had spent the last 100 years or so perfecting the most efficient technologies for gathering, storing, transmitting, and consuming solar energy. Of course, this is not the case. We’ve been busy exploring the new wonders—electricity, jet propulsion, and mountain leveling— since the industrial revolution began. You will be at the forefront of challenging the habits we have formed and the blissful ignorance of our energy use. To be prepared for this task, it is important to understand the many faces of energy use, including how it is created, transmitted, and stored. Even if you focus only on energy management in IT, branch out into renewable energy, fuel cells, and the economics behind it all. We are heavily dependent on fossil fuels for electricity. There is long-term promise, but the various approaches to solar energy along with many other renewable energy sources need to be developed further. However, the reality is that, in terms of energy generation, we are a planet fired primarily by liquid petroleum and coal. Figure 1.4 shows how we have been powering our neon lights, data centers, and iPods as a species for the past 30 years. Understanding what is behind a power outlet is enlightening when you ponder it. We are literally driving, digging, and drilling all over the planet for very dirty fuel sources. In the process, we are using massive amounts of fuel to get more fuel, and paying massive amounts of money to do so. When you look at where our energy come from, you will naturally feel inclined to place more value on its application. You will soon start to see an exhaust pipe on every server in your data center. As shown in Figure 1.5, coal is the primary fuel source in the United States for generating electricity.
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World Electrical Production by Source (TWh)
Figure 1.4 The way electrical energy is generated primarily comes from thermal energy conversion, with coal being the predominant fuel source globally.
Thermal
Hydro
Nuclear
Renewables
12000 10000 8000
U.S. Energy Information Administration, 2009
6000 4000 2000 0 1980 1990 2000
Figure 1.5 A breakdown of U.S. electrical generation puts into perspective how carbon intensive our current fuel mix really is.
Nuclear 19.28%
Other Fossil 0.60%
Hydro 6.50% Biomass 1.30% Wind 0.44% Geothermal 0.36% Solar 0.01% Other Unknown/ Purchased Fuel 0.10%
U.S. Energy Information Administration, 2008
Gas 18.77%
Oil 3.03%
Coal 49.61%
After you make the connection between the sources of fuel for your IT operation, you will probably start to look into other areas of energy use. It might sadden you to learn that we actually lose more energy through generation, distribution, and transmission than we use. Perhaps the best example of this is coal. Some estimates suggest that only 3 percent of the embedded energy in coal makes it to an electrical outlet! Furthermore, scientific evidence suggests that burning coal releases more radiation globally than all the nuclear plants combined while being
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What You Should Know about Energy 11
the largest contributor of mercury to the environment. Coal production is dirty, inefficient, and often subsidized. As the move toward a smarter grid evolves, you might soon be able to measure what percentage of your packets are coal-fired. Imagine how that might change web usage patterns, if a browser widget allowed you to easily track the GHG emissions that correlate to your web surfing. Making things visible gives users more choices and more chances to be good energy stewards. If we make the safe assumption that web usage will see a net increase in the future and, as shown in Figure 1.6, that coal use will increase simultaneously, it’s a guarantee that IT energy usage will see a net increase in total GHG contributions without better management. With improved IP-enabled energy management, IT operations will be able to reduce wasted energy. If we are to move toward a less carbon-intense future, improving current efficiencies will need to develop in parallel with renewable energy sources. China
Figure 1.6 Coal, the main source of global electrical supply, is projected to increase in selected world regions. U.S. Energy Information Administration, 2006-2009
India
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How Is It Used? The fate of the electrons we generate can turn into a paralysis through analysis wormhole if you let it become one. Keep it simple and follow the many global energy and economic indicators that are widely known. In general, doing so will serve you well in the early stages of your program and help build your credibility in executive communications. Some consultancy firms attempt to track global data storage, and some conclude that global energy use related to IT can be correlated to that. However, IT infrastructure is not instrumented well enough to tell us how much energy we are using on any given day. Figure 1.7 shows how the U.S. economy uses energy as a whole. Having a basic understanding of global energy use is important because your program ultimately seeks to make your organization’s energy use more efficient. You will need to be able to articulate how the capability to measure, monitor, and actively manage energy is relevant to the business. After all, you will be responsible for building the right instrumentation, networked intelligence, and highly automated controls to improve the current state of energy use.
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Figure 1.7 U.S. energy generation, distribution, and transmission estimates Lawrence Livermore National Laboratory
Estimated U.S. Energy Use in 2008: ~99.2 Quads Solar 0.09 Nuclear 8.45 Hydro 2.45 Wind 0.51
Net Electricity Imports
0.01 8.45 6.82 2.43
20.54
0.11
32.68 Electricity Generation 39.97
27.39
0.38
0.08
0.02
Geothermal 0.35
0.49
4.99
3.20 0.02
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Commercial 8.58
6.86
1.71 Energy Services 47.15
0.57
0.06 0.10
2.03
4.78
3.35 8.14
Coal 22.42 1.79
Residential 11.48 1.17
4.61
0.01
Natural Gas 23.34
Biomass 3.38
2.29
4.70
0.51
Rejected Energy 57.07
8.58
Industrial 23.94
19.15
0.42
20.90 0.46
0.83
0.67
0.02
26.33 Petroleum 37.15
Transportation 27.86
6.96
Be careful not to put your program in a situation where you appear to be passing judgment on how energy is used. Stay use-case agnostic until an opinion is requested. You are simply looking to establish the basics of how to manage electrical use as an IT service. The good news is that there is a lot of room for improvement. Today our energy use is like a fire hose at full stream—a new nozzle is needed before we can prioritize what fires to fight. This book focuses on that nozzle. Understanding how energy is used is also relevant in that your proposed program will need to take into account the costs and risks posed to the business. Availability of reliable energy will continue to be a challenge in the coming years. Moreover, in the coming decades, we will very likely be put into a position of prioritizing energy allocations. This will be felt across all industries and will certainly challenge the IT industry’s ability to grow in support of its core markets. Although a lot of attention is paid to the transportation segment, the top consumer of energy worldwide does not come from vehicles, but from buildings (see Figure 1.8). When it comes to energy-intense environments such as data centers and labs, the proportion of power that is used is amazing. For example, some massively scalable data centers (MSDCs) of the type large search engine providers operate use as much energy as 15,000 U.S. homes.
Figure 1.8 World energy consumption by use case
Manufacturing 25%
BOMA, 2005
Transportation 25% Buildings 50%
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Energy Accounting 13
Active IP-based energy management can also help reduce risk, especially in a cloud computing model in which computers can be moved. This is another body of work, but you should be thinking about making connections to other IT systems. It is possible to have an energy management structure that allows you to choose different energy sources across the globe and move your computer resources there. Understanding how your organization uses (and who in it uses) energy today can put you in a position of moving resources to avoid power outages in the future.
Energy Accounting This section is devoted to an overview of the current state of IT energy management. It is important to understand the way in which large organizations have traditionally approached the costs of energy. The basic accounting models in corporations in the vast majority of cases treat energy costs as nonmalleable. Of course, cost savings can be achieved by locating a business in a region with lower power costs. One can negotiate contracts with local utilities to some extent, but for the most part the mentality is to see electricity as a fixed cost. In recent years, some interesting and laudable programs and technologies have paid for themselves within months. Building-efficiency initiatives, utility rebates, and reductions in the number of people who travel all save money. However, there have been few, if any, tools to date that can dramatically reduce energy costs through broad-based active energy management. This is important to remember if your Finance department is cynical when you first approach them with a large savings opportunity for the organization related to electrical usage. Energy costs have traditionally been managed out of a real estate function within an organization and reported to the chief financial officer (CFO). The Real Estate and Facilities department has historically relied on technologies from the building management industry. The building management industry has not yet provided a network-based option that can provide the same level of ubiquity that an IP-based network can provide. This has led to a situation in which even the most basic energy management function, monitoring, is not typically available to most users. The information that is typically available is energy use by building. This information will be important to building your business case, which we cover in detail in subsequent chapters. This information will be needed to propose an allocation model between Facilities and IT departments so each group will have a financial incentive to work together to reduce wasted energy. You will very likely need to approach your CFO to make this proposal, and you will need to understand how your organization accounts for its energy usage.
Calculating Your Energy Costs It can be difficult to obtain utility costs up front simply to estimate what you’re paying for electricity in your IT operation. It can also sometimes be politically sensitive to inquire about an organization’s utility contracts. You also might not want to give away the fact that you are working on a business case until you know that it is theoretically compelling. Not to worry—there is a fairly simple way to determine a high-level cost for your energy use. The following steps can give you an order-of-magnitude cost estimate without ever having to alert anyone else in your organization:
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Asset/Inventory Lists Review your IT and/or Facilities asset lists to determine the electrical requirements of your infrastructure. Most asset tracking includes a power requirement. If it does not, you can simply get the make and model of a specific asset, obtain the data sheets from vendor websites, and aggregate the power requirements. Power Costs by Region If you’re in the United States, www.eia.doe.gov has regional and historical costs. For the European Union (EU), www.energy.eu provides the same type of information, and www.ieej.or.jp does so for the Asia-Pacific region. You will need to find out the power costs for the buildings your organization owns and leases. Operating Cost Estimation At this point, because you need to only estimate an initial savings, you can use this simple formula: operating cost = capacity × hours in a year × electrical cost × a normalization factor for nameplate versus nominal draw An example is as follows: operating cost = 1,000 kW × 8,760 hours × $0.10 per kWh × 70% Facilities Overhead If you want to include an initial estimate for the energy required by facilities infrastructure, you could use a simple ratio. The Green Grid metrics of power usage effectiveness (PUE) will give you a peak that can be averaged and will tell you how many watts go to IT versus facilities. The typical PUE we see today ranges between 2 and 2.5. This means you have 2 –2.5 watts dedicated to power and cooling for every 1 watt of IT load. Fuel Source Effectiveness The U.S. Environmental Protection Agency (EPA) recently released a new metric called energy usage effectiveness (EUE). This metric functions similarly to a PUE but traces the power path a couple of levels up the energy transference chain. Specifically, an EUE metric lets you incorporate the energy efficiency of a particular fuel source such as coal, nuclear, wind, or solar. We considered this metric as out of scope for our early phases, but it is worth investigating for data center site selection and future phases of your program. The process by which you make these steps meaningful is fairly straightforward. Ultimately, you want to break your company’s energy use down to assets and individuals with a margin-oferror target of less than 5 percent. This is most easily accomplished early on by using the associations that PUE provides and building that on top of the energy information you can access today. In later chapters, we describe how you can begin to instrument IT and facilities infrastructure to improve accuracy and help you target areas of savings. When working out an initial cost estimate for your energy use, note that the power costs found on public sites are typically listed as regional averages. Most large corporations and governmental bodies, however, negotiate better rates with utility providers. For example, we have seen some companies pay half the regional average. You can simply footnote your analysis with an exact pricing to be confirmed notation. The savings estimate will scale linearly simply by changing the kilowatt hour (kWh) pricing in your formula. Another important point to consider is the concept of efficiency at load level and kilovoltampere (kVA) versus kilowatt (kW). There will be some changes in cost based on how efficiently your infrastructure is consuming electricity. However, for an initial estimate, we recommend indicating these finer points as to be confirmed. Getting too deep into electrical efficiencies can lead to paralysis by analysis because there are significant instrumentation requirements to determine building and facilities infrastructure efficiencies. You will be able to measure IT and building efficiencies more easily after you’ve deployed IP-based monitoring.
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Energy Accounting 15
For an order- of-magnitude of savings, you can assume that power factors are unity, unity meaning that kVA equals kW. Unity enables you to use kW instead of kVA and functionally assume a 100 percent load level for electrical efficiency. Simply call out that you have done this in any initial assessment along with the power cost assumptions you’ve made. Keep in mind as you go through this analysis that it is simply the precursor to more-accurate figures you will gather later through deploying instrumentation.
Tip Don’t forget the context for the energy costs you initially estimate. You are simply looking to uncover an order-of-magnitude cost for your business. You are not looking to provide exact costs and savings up front. In the next chapter, we describe a basic benchmarking process that helps you establish an initial, high-level business case to fund a proof of concept and pilot. Be careful to position your math the right way or you can lose credibility right out of the gate.
Energy Intensity Over the past decade or so, we’ve seen a decrease in the energy intensity of corporations across a range of industries. Energy intensity is defined as the gap between GDP and a country’s or a corporation’s productivity. Because these two mirror each other, the only way to shrink this gap is through more-efficient use of energy. Because a net reduction would imply less productivity, it is unrealistic to expect an organization to use less energy. What is practical is to apply IT management systems to make more-efficient use of your organization’s energy. This will, of course, save money and help reduce energy intensity across a range of industries. Figure 1.9 shows the breakdown of global energy intensity. 1.0 Australia United Canada States Kuwait
Brazil
Japan, France, Netherlands, Italy, United Kingdom, Germany, Israel, Republic of Korea
0.8 0.7
China
0.6
South Africa India Pakistan
0.5
Russian Federation, Saudi Arabia
Zambia
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Poland Mexico
Argentina
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UNDP, 2006
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Figure 1.9 Energy use mirrors GDP at the country levels and by extension will be the same for a corporation.
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Many estimates point to IT as the reason we have seen a net reduction in energy intensity. This supports the notion that although IT uses a significant amount of energy, it is a Greener alternative than older methods of communications and collaboration. This further supports the notion that information technology, when deployed properly, can be more efficient at scale than legacy approaches. There is a scientific model called Moore’s law that you can research to help explain this concept. Basically, it shows how new systems are gradually accommodating greater workloads at the same energy requirement. Moore’s law is also instrumental in projecting power and cooling density requirements of equipment. Understanding this concept is not critical to your success but will certainly help you later, when you approach data-center-type environments. Where a large gap still exists is in the energy intensity of buildings. Generally speaking, building management systems and utility grids lack the same flexibility and scalability in their networking capabilities compared to IP-based networks. This is no slight to these approaches to energy management; they simply have not had the same interconnectivity and interoperability drivers as have IT approaches. There has also traditionally been a limited incentive structure in the building and utility industries. Although any Facilities department would want to save money, they are in the business of supporting other functions such as IT operations that are not within their direct control. Take a data center as an example. Facilities cannot turn down air-conditioning and power systems without knowing the effect on IT infrastructure. This is probably the biggest reason that in order to improve building energy intensity, you need to start with the endpoint power-consuming infrastructure and work backward into the building infrastructure.
Note There are examples where real estate and facilities operations are providing large savings independent of IT. The most obvious is the ability of a real estate operation to fine-tune electrical and mechanical systems to incrementally improve buildings that are over provisioned or generally running inefficiently. Many utility rebate programs target these types of improvements. What you are positioning in the form of native, IP-enabled energy management can take advantage of these programs. However, what you are positioning is fundamentally different from facilities improvements. You are going to the source (the IT load) and treating the “disease” and not the “symptom.”
Cost Allocations Many vendors are pitching an energy-saving technology without knowing a simple fact: IT rarely pays the power bill. In most cases, an organization structures a bill-back model whereby an IT operation, or more likely each business unit, pays some percentage of the power bill. Even in these cases, those costs rarely trickle down to middle managers. These models are usually built at executive levels and are taken as a somewhat fixed cost. Determining how your organization handles the power bill is critically important to building credibility as well as targeting the right stakeholders. A value proposition of we’ll save you money rarely appeals to IT managers. Better capacity planning and Green branding might, but it is in everyone’s best interest that you have your current energy cost allocations mapped out ahead of time. After you do, you can better articulate options and provide incentives for facilities and IT departments to work together to implement energy management. Keeping in mind that change can be difficult, you will likely need to propose a change to the current energy accounting systems used by your organization. This goes back to the importance of effectively articulating your value proposition. As you will see in later chapters, a 10–20 percent reduction in total energy use related to IT is achievable. This should be enough for any CFO to agree to a savings-sharing model between Facilities groups and IT. Figure 1.10 shows the
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Energy Use in the Digital Age 17
framework we built well in advance of proposing an energy savings plan. Without this, you are likely get a lukewarm response or worse and be seen as not understanding the current allocation models for your business.
Figure 1.10 Understanding how facilities and IT departments currently operate in terms of energy costs is critically important to your success in proposing a management plan.
Carbon Chief Financial Office $
Accounting
$
IT and Labs
Facilities and Real Estate Energy Management Team
Network Management Building Management
Consulting Engineering
Partners and Industry Associations
Asset Management
Energy Use in the Digital Age Perhaps the best study on IT’s energy use at a high level to date was conducted by Gartner 2007. This study puts into perspective the amount of energy used by the world’s IT systems. The study is comprehensive in that it covers distributed computing (desktops and laptops) and centralized computing (data centers). What is shocking to many is that IT’s global greenhouse gas loading resulting from electrical use is equivalent to that of the airline industry. It doesn’t sound like a lot when you say IT represents 2 percent of all global emissions until you realize that this is equal to all the flights all over the globe each year (Figure 1.11). Why is this the case? Well, we like our gadgets, and we burn a lot—and we mean a lot—of coal to power them.
Figure 1.11 Electricity used by the IT industry and the GHG emissions that correlate are comparable to the emissions contribution of the airline industry. Gartner, 2007
Manufacturing 25%
Transportation 25% Buildings 50%
Recognizing the magnitude of emissions resulting from electrical consumption on the whole might have you seeing a figurative exhaust pipe on every power source in your home or office. Figure 1.12 shows a breakdown of global energy sources indicating that 45.1 percent of global
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emissions results from electricity and heating. There is much discussion in professional data center circles that IT’s actual consumption may be double the Gartner estimation. The simple fact is we can’t say with a high degree of confidence what IT systems use globally because their energy use has yet to be fully instrumented, networked, and normalized. We have the technology available today to change this power-blind state.
Figure 1.12 Energy used by buildings and the IT systems therein produces more than twice the greenhouse gasses as all transportation combined.
IT 2%
Rest of Industries
Climate Analysis Indicators Tool (CAIT) version 7 (Washington, DC: World Resources Institute, 2010)
It should be noted that IT is already working toward, and will continue to facilitate, reduced energy use or simply using it more efficiently. Email versus snail mail is the best example of this. Many solutions that are branded as Green, when implemented properly, can improve efficiency. However, without common monitoring and measurement systems, there will be no common management. Digging into the problem broadens your thinking on not only the technological elements of climate change but the sociobehavioral elements as well. The next logical questions you might ask are, What is IT used for today by software application? and What do we use all this energy for today? These are topics for another book, but they are interesting questions to ask. The approach we’ve outlined in this book, if adopted, will broadly move the industry toward not having to guess at our energy use. If we adopt a standardized approach in the most pervasive layer (the network), nothing can stop the IT industry from serving as a model for other industries in monitoring and managing energy use. This IT innovation is needed more in this area than in any other today. By managing energy use by IT, we will be learning lessons on how to monitor non-IT systems such as mechanical and electrical building infrastructure. A second large area of savings related to IT energy use is on the facilities side. A lot of emerging technology is being developed on the building side, but again that is a topic for another book.
Assessing Capacity Your corporation’s energy use might already be constrained in certain regions. A useful exercise to assess where your corporation stands in terms of growth restraints related to energy is to determine your current energy usage by region and by building. Take the current use and compare it against historical data for energy use and business productivity. You can make an accurate assumption of your business’s growth compared to energy use and forecast when your organization will experience constraints in certain buildings and regions. In the United States, you can also use the EPA power profiler at www.epa.gov/grnpower/buygp/powerprofiler.htm to determine each building’s energy and, by extension, carbon intensity.
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Energy Use in the Digital Age 19
How Is It Being Used Today? Although much attention is paid to data centers, relatively little attention is paid to the energy used by distributed computing. It is difficult to say exactly why this is so when you consider that there is roughly an even spilt between the two in terms of energy use (see Figure 1.13). One theory of why we focus on data centers is that they represent a centralization of the problem. In addition, a lot of money is at play in data center environments. What is ironic is that non-datacenter environments are a much better place to begin managing energy via IP. Why? Quite simply it is because data centers tend to be 24/7, mission-critical environments. In later sections, we show how taking an edge-in approach mitigates risk in adopting energy management.
Figure 1.13 Total energy use in IT is estimated to be split roughly between centralized and distributed computing environments.
Data Center
Desktops
Cisco Systems, 2008
It is uncertain how total energy requirements for data centers will change over time, but virtualization and moves toward a cloud model have little effect on linear energy use (see Figure 1.14). Energy allocations will simply shift within the infrastructure of the data center. 140
Projections coming from the EPA’s response to the House of Representatives shows a relatively constant rise in capacity requirements across a range of data center types. U.S. Environmental Protection Agency Energy Star Program. “Report to Congress on Server and Data Center Energy Efficiency,” Public Law 109431, August 2, 2007.
Annual electricity use (billion kWh/year)
Figure 1.14
124.5 120
Server closet Server room
100
Localized data center
80 60
61.4
Mid-tier data center
40 20 0
enterprise-class data center 2006
2007
2008
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2010
Projected Electricity Use, by Space Type, Historical Trends Scenario, 2007 to 2011
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Within the four walls of the data center, some variation exists, but experience tells us that most operations are still server-centric in their makeup and, by extension, their energy use. Virtualization and aspirations of software as a service (SaaS) are shifting this. As cloud computing evolves, it will naturally move the power allocation from servers to storage and networking. It might also introduce elements of complexity to energy management, which is covered later in this chapter. Industry studies suggest that storage systems’ energy allotments have already surpassed those of servers. Surveys we’ve conducted show that energy allocations in U.S. data centers are still heavy on computing (see Figure 1.15). 50
Figure 1.15 Typical breakdown of energy requirements within a data center
45 40 35
Cisco Efficiency Assurance Program user survey, December 2008
30 25 20 15 10 5 0
Compute
Storage
Networking
Although computing still appears to be the largest consumer of energy within IT, some recent studies suggest that storage is quickly claiming this dubious distinction. The growth of storage shows no signs of slowing. Figure 1.16 shows how storage requirements are trending. 1,000,000
Figure 1.16 IDC projects that data storage requirements in the IT industry are quickly outpacing available capacity. IDC, 2008
900,000
Information
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Energy Use in the Digital Age 21
There is a big difference in energy requirements based on the type of storage infrastructure you use. Tape uses far less electricity over its life cycle than disk. However, tape has limitations that will continue to hinder its adoption for some use cases. In general, storage will need to be tiered in order to curb its growth rates and enable users to manage their data more efficiently. A fairly sound argument from vendors focusing on Information Lifecycle Management (ILM) is that energy management starts with your information management strategy. What is critically important to understand is the interrelationship between corporate productivity, digital enablement, and the volatility we are starting to see in global energy markets. Every year since the commercialization of computing began in the early ’80’s, governments, corporations, and individual consumers have become more and more digitally enabled. It can be inferred that this trend will continue, and if it does, the energy (and emissions that correlate) resulting from digital enablement will be increasingly scrutinized for a range of reasons. Energy usage, physical location, and physical security will be valuable data sets moving forward, whether you manage a data center or desktops.
Form Follows Function A good example of how and why IT systems get built out the way they do is the iPhone. This endpoint device has taken on new functions that require a new infrastructure to support it. Some iPhones have 32 GB on board and server-esque video requirements. The AT&T data centers that support the iPhone will, of course, need to be redesigned to accommodate greater server and storage requirements based on the performance capabilities of this new endpoint device. You can spend a lot of time researching your company’s energy use to know what is fairly apparent by just looking around. In our case, at Cisco Systems we are larger in terms of networking (infrastructure) and on labs (operations). That is unsurprising when you consider what Cisco does.
Emerging Technologies Some large technology vendors are investing in solutions with real implications for enterpriselevel energy efficiency. However, for the most part, the investment has not caught up with the hype. It is just too early to expect major technology shifts across computing, storage, and networking because of loosely defined Green social pressures. Many in the industry are just arriving at the conclusion that managing energy can open up internal funding for other Greenrelated challenges. This section provides a high-level overview of the technologies you can use today to drive energy efficiency into your organization. Knowing what technologies will be in your scope of management and interoperability will be necessary for your program to scale. There are many approaches to improving IT’s operative efficiency. We chose to provide a software application—computing and instrumentation-level overview. Depending on how ambitious you want to be with your program, you can include these in your management scope. Otherwise, you should at least look to include them in your scope of interoperability.
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Enterprise Management Applications Technology is being actively developed that shows promise within existing enterprise management applications. IBM Systems Director and HP Systems Insight Manager both provide a high degree of granularity for server deployments and roll up seamlessly into Tivoli and OpenView products respectively. Both of these applications also provide ambient operating temperatures of chassis, which can also be useful in thermal management. What will likely transpire is a blending of a network-based approach with that of other leading IT players. IBM has already announced it will adopt elements of Cisco EnergyWise, and many other companies are already following suit. The industry is still in discovery mode on how energy will be managed within IT domains. However, today there is no one-stop shop for an enterprise-level management solution, either stand-alone or integrated. Having the right “plug-ins” to existing enterprise management applications will be critically important to limiting the disruption of IT adopting energy as a service. These large management applications are where most IT operations aggregate and report on data across a range of attributes. Integrating energy into this approach versus building a new “silo” is preferable for most large operators. What remains to be seen is the level of detail that users want and when. This is where other popular themes such as virtualization and cloud computing could serve as a driver for better energy management.
Virtualization and Mobility It is hard to say how high or low computing and storage utilization is at a global scale on any given day. Some estimates indicate that servers are utilized in the 15 percent range and storage at somewhere around 35 percent globally. Utilization is in this case being defined as a percentage of a total workload capacity. For example, a grouping of 100 servers at an average of 15 percent utilization means that in essence the work being performed by 100 physical servers at 15 percent utilization could be done by 15 servers at 100 percent utilization. To avoid any controversy, let’s treat these as arbitrary figures and say that by implementing virtualization technologies, you can bring both segments of infrastructure into the realm of 70 percent utilization. Forget the watts, dollars, and CO2 for a moment, and simply look at the physical server units you could remove. In a document released by McKinsey & Company consulting in November 2008 entitled “Data Centers: How to Cut Carbon Emissions and Cost,” a user reduced their compute requirement by 15 percent. This report can be found on www.mickinsey.com. This use case equated to 5,000 server units. Based in part on this study, a computing shutdown company, 1E, released a paper that extrapolated this example with some other leading research and estimated the following: •u Current global energy wasted due to underutilized computing = 12,672,000,000 watts •u Current global energy wasted due to underutilized computing based on U.S. average util-
ity rates (which are lower than global averages) = $5,323,761 daily How energy efficient you can get with virtualization and how much risk you accept all depend on how you design and manage virtualization deployments within the context of your specific operation. Data centers are like natural ecosystems in that a change in one area very well could have unintended consequences somewhere else. On this note, there is a key point to
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Energy Use in the Digital Age 23
keep in mind when considering large-scale virtualization projects: You don’t hear this much in the IT world, but the dynamic computing and storage loads that virtualization delivers have a power and cooling corollary. Managing dynamic electrical and thermal loads will be a growing challenge as virtualization grows. VMware calls their approach VMotion, and it allows an administrator to move groups of virtual machines (VMs) called Vblocks across the network. When you get to a point where you have multiple kilowatts worth of virtualized load moving within a single data center facility or between facilities, you might face some major issues. This is the case not just for the obvious reasons, where you might have mobile cooling hot spots to manage, but because legacy Uninterruptible Power Supply (UPS) and air-conditioning systems in general are designed not just for a high end but also for a low end of utilization. In a three-phase environment, these systems can run inefficiently at low loads and can also become unstable. This scenario again points to the need to work closely with your facilities professionals or perhaps even to hire a few. It also shows how having the capability to check power and cooling profiles prior to provisioning new VMs is highly preferable. The good news is you can design a data center to accommodate these large load variances. The key here is the ability to design data center critical facilities into highly modular zones with different operative profiles but within the same building, campus, or enterprise. Prior to the advent of more-conducive facilities modularity around 2003, this was very difficult to do. Now you can target your facilities design more easily to accommodate high-density, high-efficiency IP manageability and in smaller power increments than previously. The biggest challenge is perhaps building a data center team that can design this type of facility in alignment with fastpaced technology. Leaving aside the obvious bandwidth and energy challenges in moving 40–50 GB virtual machines all over the planet, this type of computing mobility could make energy markets very interesting. There has been a lot of discussion around the cool factors of this approach, such as follow the sun or follow the moon computing. This scenario applies to energy perfectly. Will China’s next big commoditization wave be around coal-fired data center service delivery? It’s hard to say, but it points to why the efforts to manage energy and the emissions that correlate need to be international with governments at the table. These efforts need be supported by real data and in close to real time.
Sensor Networks Another area to keep an eye on is instrumentation. There has been a lot of development, at least in data centers, around temperature sensing. SynapSense and Arch Rock are two companies that are having success in providing easily integrated temperature, humidity, and barometric sensor networks. These sensors can be particularly useful in managing cooling costs in data centers and labs. Cooling is typically the largest cost in a data center (see Figure 1.17). What remains to be solved in sensor networks is a low-cost solution that accommodates granular IT asset telemetry. Telemetry is particularly important to manage air distribution and basic asset inventory. This is an area many large manufacturers and specialized companies like SynapSense are looking into, and a solution should be imminent. The last area to consider in how IT assets can report their energy use is what to do with all the information coming back from the IP stack, buildings, and sensors. Over time, a lot of data is
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coming back that will need to be networked, stored, and interpreted. In upcoming chapters, we cover several database elements. In addition to storage, the orchestration applications for sensor data and the load and inputs from a utility provider are already being built.
Figure 1.17 A driver for recent development in sensor networks has been cooling costs. Electrical supply for HVAC is typically the number one operative cost for data center environments.
Conversion Loss 10%
Lighting 3%
ICT Infra 37%
Cooling 50%
Cisco Efficiency Assurance Program user survey, December 2008
Some large data center operators, such as Aperture Technologies and Rackwise, are already using solutions for geospatial asset inventory management and blending them with active temperature-sensor networks. Further integrating wattage allocation for IT and facilities loads will paint a more complete picture of your environmental factors (see Figure 1.18). The one area yet to be broadly incorporated into an active geospatial management is asset telemetry. Telemetry by physical asset via wireless or wired connectivity is just too expensive today. When a low-cost, nondisruptive solution for physical telemetry is available, we’ll have more or less a complete package. This is where Google Earth and the approach it has taken will likely have an integral role as a management front end at certain levels.
Figure 1.18 Using 6sigma Room and Rack, Emerson was able to provide us with thermal imaging over a geospatially accurate data center model. Emerson Network Power Data Center Efficiency Assessment document prepared for Cisco IT, 2009
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The State of Energy Management Today 25
The State of Energy Management Today Historically speaking, energy has not been actively managed both at the corporate and governmental levels. There have been few drivers for energy monitoring and management to date. Large corporations in the United States and industrialized nations in general have experienced very few energy-related barriers to growth since the invention of computing. Our consumption has only started to be challenged in recent years. This section takes you through a high- and mid-level overview of what aspects of energy management are available today. Because it is a very new focus area for any large organization, we call out some of the elements that can go into a codified energy management program. As mentioned in earlier sections, you will need to decide what elements you want to cover in the program that you develop. If you compare how we manage our energy use to how we manage critical financial data, one could argue that we don’t manage it all. The energy industry in general has yet to be IP enabled, and that may surprise you. Basic Ethernet connectivity does not exist across the grid or to the components that generate and convert electricity. However, a lot of work is being done to define what attributes can and should be managed. Generally, they break out as follows: Infrastructure Whether a component is an IT product (server, storage array, or switch) or a home, there is a recognized need for better instrumentation in the infrastructure that consumes energy. For IT assets, the power supplies in a chassis are the main points of power consumption and rarely report their energy use to an operating system. For a home, power is primarily consumed by large appliances and HVAC systems. Some large vendors fear that the increased cost of this instrumentation will hamper their ability to be competitive in the market. Electrical Distribution Whether you think of distribution within the classic notion of a power grid or the electrical distribution within a building, there is general acceptance that distribution losses can be mitigated through better instrumentation and management. Some estimates say that as much as 10 percent of grid power is lost in distribution. This is about the same percentage we see in data center environments that use step-down transformers rather than direct high-voltage distribution. Utility Demand Response Grid operators are relying on comparatively antiquated methods to deliver energy to homes and businesses. Each time a new home or office building is constructed, an estimated capacity requirement is allocated. This benchmark is then measured against incremental meter readings and compared for accuracy. For many grid operators, this still involves a truck roll—a person visiting a site and taking a reading. Demand response today does not rely on Internet protocols; rather it relies on SCADA networks that were primarily built in the 1960s. Although other areas are being examined, these are the macro buckets of developmental interest. Wherever you look at how we use energy today, you will see massive opportunities for improvement. You can achieve these improvements by knowing where to use what tools. The deeper you get, the more you will see the reasons we advocate IP-based networks as the right physical domain to manage energy use across a range of entities and activities. We dive into this further in the “Smart Loads” section of this chapter.
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The Scope of Management Improving management of electrical generation, distribution, and consumption will be fairly straightforward in that power plants, power lines, and many IT assets don’t physically move. However, to manage emissions directly will be a much larger challenge. To put direct emissions management in perspective, consider that in 2005 the U.S. EPA reported that, on certain days, almost 25 percent of the particulate matter making up the smog in Los Angeles originated in China. Yes, a quarter of the smog in L.A. comes from China. Although this level of monitoring and management will be difficult, energy management efforts will teach us valuable lessons to manage other areas directly such as emissions, pollutants, and water usage.
Where Is Energy Management Technology Adoption Today? Some progressive organizations are already adopting newer, active energy management technologies. This section focuses on the state of energy management, not at the country or global level but within the corporate context. As you drill down from the macro level to the specifics, you will see some common themes, but you will find a much higher degree of complexity when you approach the individual use case. For large organizations, energy management can be a starting point and funding source for larger initiatives to become more sustainable. The concept of sustainability is quickly replacing the notion of Green for most companies that are serious about making real changes. It is difficult to say with certainty what people are individually thinking in terms of sustainability in a business context. This is particularly the case in IT, as knowledge workers change roles and companies often, as compared to other industries. Gone are the days of working for the same company for an entire career and relying on their pension for your later years. This lends itself to a situation whereby few people even think, How sustainable is my company? Rather, it is the norm to ask, How profitable were we this quarter, are there any layoffs on the horizon, and am I being paid what I’m worth? That mentality is simply the reality today. Trying to influence your organization to be proactive about sustainability will prove to be difficult at best, in our experience. What is more realistic is to point out some of the contributing elements to sustainability and to work in shorter timeframes. Primarily, you will want to understand the interrelationship of economics, energy, and risk for your organization. Energy management will have an impact across all these elements, and it can remove barriers that already exist. The most obvious example of a barrier we see today is power- and cooling-capacity constraints in data centers all over the world. These benefits can be articulated at a high level: Energy Costs Energy management can provide as much as a 20 percent total reduction in electrical costs to the enterprise. Carbon Costs Although not globally pervasive, compulsory carbon reporting has already been implemented and taxation systems are being developed by countries such as the United Kingdom and Australia. The United States is currently considering a cap and trade system that could increase costs for businesses that aren’t actively managing their energy use. Upgrade Costs The vast majority of IT operations—perhaps even all—are using much more energy than they really need. If there were a better system of on-demand energy use, you
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could argue that you could defer the costs of acquiring new infrastructure and electrical supply in the mid-term. Energy Reliability Although deploying energy management carries a significant risk of human error, that can be mitigated, as we discuss in later chapters. Today there is little insight into electrical usage patterns within a business. Simply deploying energy instrumentation will improve your current risk model by providing basic insight into electrical supply. Usage trends, temperature, and branch circuit monitoring can all be useful data sets in troubleshooting energy-related problems. Energy monitoring can also enable you to assess the real-time risk to electrical supply for your organization from a central location. Central management of energy reliability allows for modeling of some changes to the operations. Capacity Management A significant challenge for any large organization that is even moderately IT dependent is forecasting the electrical and thermal requirements of the IT and facilities infrastructure that supports the IT operation. This is largely highly educated guesswork, but by implementing energy management, you are providing a foundation of real data to enable better guesswork. Many other business functions will be affected by implementing energy management. However, unless you are a governmental organization, you are most likely looking at sustainability in purely financial terms. Although you might not be able to change this anytime soon, you can move your organization toward better visibility and management of resources. Again, you can adopt a politically neutral position by simply exposing the financial costs. After you are monitoring your energy use enterprise-wide, it becomes a fairly simple exercise of converting that data into an ecological context. You will find that these conversions of wattage reductions into cost savings will further support your efforts to educate your stakeholders on the broader value of energy management.
Energy Sourcing As Benjamin Franklin famously said, An ounce of prevention is worth a pound of cure. This applies perfectly to choosing the right fuel source for a new building. There is an extensive matrix of considerations involved in site selection for a business when new capacity is needed. You look at costs, risk, proximity to recruitment centers, data, power, water, and so on. Up until very recently, few organizations have ever looked at the type of electrical production found in a particular geography. As you build credibility in the organization as an energy manager, you will likely be asked to provide guidance on site selection. Knowing what type of power is available and in what region will help you provide guidance on this subject. If you want to move your business toward cleaner fuels but avoid a debate on global warming, simply point to the carbon intensity of a fuel source in financial terms. It is clear that governments across the globe are at least considering some form of emissions regulation. Because emissions from electrical generation are among the highest in the world, you can assume that businesses will be required to account for their CO2 contributions in the near future. In addition to increased costs resulting from regulation, you can point out the volatility of energy markets over the past few years (see Figure 1.19). Although much attention is paid to gasoline price volatility, most people don’t realize that the costs of coal have doubled in the past five years. Furthermore, some estimates predict that a cap and trade system in the United States could add 7–18 percent to electrical costs.
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Figure 1.19 Energy market volatility and energy security concerns are increasing demand for clean and reliable energy that can be sourced domestically. International Energy Agency, 2006
100 90 80 70
Northwest Europe market price Japan coking coal import cif price US Central Appalachian coal spot price index Japan steam coal import cif price
60 50 40 30 20 10 0
1995
2004
What you will soon realize as you examine the energy-sourcing options for your organization is that there are few locations where you can place a business that have clean energy sources. You can argue that in the United States, the Pacific Northwest is ideal given its large amount of hydroelectric power in some areas. However, much of this hydroelectric power is dependent on snowcap melt, which is dwindling rapidly as the climate warms. Nuclear energy is cleaner, but has a host of other political issues with which it must contend. In my experience, there is no perfect place to site an energy-intensive operation. It is a lesser-of-evils argument, and you should present the trade-offs if asked to consult in this highly strategic business decision.
Avoiding Coal Fired Packets Many valuable and publicly available tools exist that can help you with site selection. Google Earth is a good tool for this purpose. Roughly 70 percent of what you typically need for site selection is already there—roads, civic infrastructure, seismic zones, tornados, hurricanes, and proximity to recruitment centers. Google Earth can also be a good executive presentation tool in that it is highly customizable. To complement this tool, the EPA’s eGRID site is also valuable in determining the carbon intensity of a region’s fuel source (http://cfpub.epa.gov/egridweb/ghg.cfm). This site serves as a valuable tool for planning and comparative analysis. Finally, The Green Grid provides maps that help data center operators target locations with the right temperatures to leverage “free” cooling (http://thegreengrid.org).
Renewable Energy You will hear many arguments about renewable energy. One could argue that, by definition, petroleum is a renewable resource. However, it takes hundreds of millions of years to renew. The more-popular notions of renewable energy relate to solar, geothermal, wind, and oceanic sources. Some attention also is being paid to biological processes as a means of generating
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energy. A recently developed approach called the microbial fuel cell (MFC) is being deployed in pilot tests throughout sub-Saharan Africa. The MFC extracts a trickle charge of current from bacterial metabolic activity within a bucket of mud. The fuel cell can in turn power lighting and charge cell-phone batteries. However, all of these approaches have significant capacity challenges when you consider the current trends of energy requirements globally. Up until Bloom Energy’s recent breakthroughs on fuel-cell technologies, storage has also been a fundamental, technological challenge. In addition to the challenges in generating enough energy to meet demand, there is also the issue of government subsidies for coal, oil, and gas. At present, petroleum-based energy is provided to the public at artificially low costs. This is a major factor in why coal is still king and, if left unchecked, will continue to be for years to come (see Figure 1.20). Many energy experts agree that if the U.S. federal government leveled the playing field between petroleum, solar, and wind generation, there would be a major shift toward renewable energy options commensurate with public opinion. In a pure democracy, public opinion would drive costs down naturally. But representative democracy in the United States might prove to be an obstacle to opinion influencing costs because a lot of money is at stake for the oil, gas, and coal industries. It will take progressive political leadership and a vested interest by these industries for a shift to renewable technology to get a fair shake. The EU does not subsidize fossil fuels as heavily, and in turn, there are more public transportation options, greater renewable energy generation, and better awareness in general of the costs and security issues related to petroleum as a fuel source. Until governments put in the economic drivers to change our fuel sources, coal will continue to be king, and other cleaner approaches will be hindered in their ability to grow market share. However, if we assume that a cap and trade or other carbon management system is implemented, there should be a leveling of costs between carbon-intensive and low-carbon fuel sources. This is driving many data center operators to consider not just the current cost of electricity in a region, but what the future cost might be after additional carbon costs are included. Figure 1.21 shows a map of the United States highlighting the carbon intensity of fuel sources. Key economic indicators, such as venture capital investment in solar, wind, and corn-based ethanol, all point to the beginnings of a larger market. How this market tracks against technological investment remains to be seen. It is encouraging that many large IT manufacturers, search-engine operators, and software developers are investing in cleaner technology solutions. Although still small by comparison to other areas of their businesses, it is at the very least commensurate with the markets they serve. We do not claim to be experts on renewable energy generation, but solar in all its permutations should be explored to the fullest if you look at energy with efficiency in mind. It is, in theory, the most logical choice in which to invest heavily. Because the limitations in harnessing solar energy are many, we need solutions to replace our current methods. There is, however, enough solar energy striking Earth in a single hour to power our current consumption for a year. Nevertheless, we have yet to perfect the technology to capture it. Much encouraging work is being done in photovoltaic solar and wind turbine technology. Heliostatic solar, or solar concentrating, plants are showing promising results in Spain and more recently in the United States in providing respectable capacity at competitive prices. The industry is moving along, but it is still in a very immature stage. There has been no real macro-market definition of utility-grade renewable energy to date and certainly no consolidation of technology to achieve economies of scale. The capacity capabilities at current pricing are just not attractive enough for most organizations today.
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Figure 1.20 British Petroleum reported that, as of 2009, coal is still the fastest-growing fuel source globally for electrical generation. Given that there are many years of proven coal reserves worldwide, coal’s leadership position will be challenged only through government intervention. British Petroleum, 2009
Production Million tonnes cif equivalent 2100 1950 1800
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Middle East and Africa
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Consumption Million tonnes cif equivalent 2100 1950 1800
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Figure 1.21 Purdue University’s Vulcan project provides a good illustration of where emissions sources are concentrated across the United States in terms of millions of metric tons of CO2. Purdue University, 2009
As renewable generation technologies improve and if governments can level the economic playing field, we can hope that it will be a growth market. As a person with an IT and facilities background, I believe there will be a large role for energy management relating to fuel types. As an energy manager, you would be responsible for working with a utility to gather data on residential and commercial customer consumption and generation of energy. To date, only consumption has been a serious focus for utilities. If a home or business wants to sell back excess energy, there are no IP-based, automated, and open management applications available to do this. Even if there were, no network architectures and instrumentation options exist for the infrastructure involved in this scenario. This will be another opportunity for IT and facilities investment in integrated solutions.
Heliostatic Solar Much encouraging work is being done on heliostatic solar, or solar-concentrating, plants. These steam turbine plants use mirrors to focus the sun’s rays onto a single point. The temperatures are great enough to turn H2O into steam and drive a traditional electrical turbine. Early generations of these plants are showing respectable capacity at competitive prices in Spain and in the United States. Within the current global free-market system, the only way for renewable generation of electricity to compete is on a cost basis. If you work for a publicly traded company, you can safely assume your recommendations will be looked at in the same way.
Rebates and Offsets A lot of options are available in the market that are nondisruptive to a business and promise Green value. We debated whether we should even include a section in this book about them because our aim is to be objective. There is a lot of subjectivity on the value and implication
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of utility rebate programs and renewable energy credits (RECs). As an energy manager, it is important to understand the issues for each. Utility rebate programs such as the one Pacific Gas and Electric Company (PG&E) runs in California offer a portion of your energy bill paid back to you based on a before and after measurement. This measurement is based on PG&E’s definition of electrical efficiency and is targeted at large computing environments. The benefits of this program are cost reductions, of course, and the savings from those cost reductions can be rolled over and reinvested into energy management initiatives. Programs such as this also raise the visibility of power as an area of savings to IT, which might be missing opportunities for savings with no extra effort. For example, IT operations all over the world are virtualizing servers for non-energy-related reasons and missing a savings opportunity. Although not a utility rebate program per say, the National Renewable Energy Laboratory (NREL) proposal called zero-energy building (ZEB) seems to offer the most promise. In this system, a particular building could achieve a 100 percent ZEB score only by supplying as much energy back to the grid as it consumes. The only way to achieve a perfect score would be through on-site generation, but because it’s a sliding scale, you achieve savings that are directly proportionate to your baseline. Some technology barriers still need to be worked through to fully automate this type of system, but it appears to be the best option available at a federal level. The last and most embattled option are RECs, often called carbon offsets. The basic framework behind this approach is to focus money and time on carbon sequestration. To capture CO2 and store it, you can do a few basic, low-cost things. You can plant trees, build renewable generation plants, or invest in new research in the field. This is the methodology behind the offering of a REC by a third party to a power-consuming business. As a business, you pay this party and hope the money gets where it is supposed to go. The detractors to this approach don’t focus on just the obvious potential for corruption but the fact that a business that buys RECs doesn’t change its behavior. Many recognize RECs as simply buying your way out of the problem. It may get to that point, but a sound argument could be made that those funds would be better spent driving real changes into the business.
The Future of Energy Management The drivers of energy management are becoming clear to many. Security, health, and economic interests are all significantly affected by how we procure and consume energy. Now that the average consumer recognizes this point, there is a burgeoning demand to take more control of one’s own energy footprint. Home users have an interest in choosing the type of power they use, businesses want to reduce their risk of outages, and every nation, of course, wants energy security. The time has arrived, and the circumstances scream out for the type of innovation and automated management that has become commonplace in IT. Whether you are an IT or facilities professional, it is important to understand the security, health, and economic drivers and the demand for practical solutions if you are seeking to manage energy as a service (EaaS). We already have an incredible amount of instrumentation everywhere we look in our lives. We see a fuel gauge in our car, weather data on our watches, and a battery meter on our phones. As I write this chapter, I see by percentage how much runtime is left in my laptop battery. On the side of your home, a spinning wheel measures your electrical usage, and most utilities still roll a truck to read it. What is missing from this picture? All these local data points have yet to be networked. They have yet to be standardized. This is behind the concept of a smart load or micro-grid. These refer to a networked group of entities that can exchange data and accept commands in a standardized way for energy monitoring and control.
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Smart Loads The concept of a load typically applies to electrical, thermal, and data constructs. A Facilities department will typically plan for it in terms of the thermal/mechanical and electrical load of a building and the infrastructure within it. Table 1.1 shows an overview of some common planning elements for IT and facilities loads that we’ve employed in mapping management options. All three of these electrical loads are interdependent and will be the first areas that can be monitored and managed via Internet protocols. For infrastructure that does not have native IP reporting capabilities, some infrastructure investment is required to convert protocols. Additional investment is also required to normalize data for reporting purposes.
Table 1.1:
Three load categories that can be actively managed via IP Critical IT
Supporting Facilities
HVAC
Electrical
Typically referred to as the total wattage requirement by computing, storage, and networking infrastructure. This data set will tell you what useful energy is being consumed by IT systems.
Wattage allocated to mechanical, electrical, and plumbing infrastructure to support occupancy requirements. Monitoring this data will show how efficiently the facility is supporting IT.
Typically, the largest subset of the supporting facilities load. Heat removal for high-density environments needs to be measured separately. Can also be measured in wattage allocation.
Protocols
Internet protocols are predominant, and most operating systems will provide electrical consumption data.
A wide variance, but predominantly Modbus. Protocols have not been built for wide area networking.
Same as supporting facilities, and instrumentation options vary broadly. Electrical use is typically available.
Interface
Primarily RJ45 Ethernet, Fibre, and Wireless.
Primarily RS-232/485 and RJ45, Ethernet is expanding.
Primarily RS-232/485 and RJ45, Ethernet is expanding.
Power usage information is commonly found in the vast majority of operating systems that have been written specifically for a hardware platform. Many larger buildings have local reporting of at least electrical meter information. Even automobiles, particularly large fleets, have onboard computers that are networked and track a variety of data. Much of the data on energy consumption is already available; it’s just a question of accessing, aggregating, storing, and reporting it. It will be some time before there is as high a degree of standardization in power reporting, but it would appear that the Internet Protocols are the logical choice to do so. IP is pervasive globally, and network interface capabilities are at their lowest price points in history. Of course, every operating system has its proprietary barriers. IBM, Hewlett-Packard Company (HP), Sun Microsystems, and Dell all report their energy use in different ways and to their classes of management platforms. Although efforts are underway (such as The Green Grid) to agree on some level of open standardization, it is understandably slow going. In addition to the server manufacturers, storage and networking equipment similarly reports energy
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usage in different ways. These proprietary barriers, coupled with the challenges of internetwork reporting across infrastructure domains, are what led us and a handful of colleagues to develop network-based management of loads. After all, the network is already there, and it has access into operating systems to route and switch data.
The Move to Smarter Loads Just as the name implies, a smart load is a power-consuming entity that can natively report (through its operating system) its energy by using existing Internet Protocols. A smart load requires no external instrumentation, such as a smart power rail, and little if any normalization and conversion of data. What is incredible is that the data that underpins a smart load has been there for years. It just hasn’t been networked until now. Look at the user interface of whatever operating system you use for computing—Mac, PC, Linux, or otherwise. As the local user, you can see information on energy use already. You can see whether your computer is charging, and if you dig down, you can find out energy information for the hardware on which that operating system is running. So the question we initially asked was why can’t that data simply be gathered in the same way we are already gathering network management data. With its large installed base of switch and routing infrastructure, who else could do it at the scale of a Cisco? These were some of the early questions we asked. At Cisco, we saw in late 2005 that a big missing piece of many management applications was the capability to accurately measure and monitor thermal and electrical use. We were forced to use “back of napkin” thinking to model changes to our data centers. On another front, we had divisions working on Power over Ethernet (PoE) applications along with other large serviceprovider chassis groups. We had enough general awareness and pockets of deep expertise in energy that we were able to get support to tackle energy management as a new focus area. As we started to study the management of energy at the wide area network (WAN) level, it became clear that abstracting data on watts could be done with near ubiquity across devices attached to the network. This is when John Parello and I met. John was the eventual creator of EnergyWise. He worked with Matt Laherty, the program manager, and Tirth Ghose, his technical partner at Cisco. John had ideas on how to use the network as a platform for energy management. He was looking at it from the edge in, and I was looking at it from the data center out. John’s background is in computer science, and mine is in facilities, and so we were off. We all happened to be thinking of the same challenge—that is, common monitoring and reporting of energy use as the first design challenge and control as the second. We all brought unique backgrounds to the project but agreed on this as the scope. To keep things in context, it is important to remember where IP-based energy management was in 2006. It was where it had been since 1998, when some of the first network-manageable power distribution units (PDUs) for data centers started shipping (see Figure 1.22). The adoption of these PDUs had been fairly slow. This had to do with how these rails were administered. Because a power cable has no communications capabilities, these rails require a user to name a piece of equipment according to the power outlet on the rail it is plugged into. After this is done, you can access the PDU through a browser and see electrical current by outlet and cycle power if desired. The biggest challenge to this approach is that it is very static; if you move an asset, you must rename and reassign it manually. This process is administratively intensive and consumes close to 1.5 times the costs of a nonmanageable power rail. Thus the adoption of this approach has been limited.
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Figure 1.22 The first attempts at IP-based energy management involved placing a network interface card on in-rack PDUs for data center environments.
We started worked on defining a smart load’s attributes in 2007. The fundamental question we were trying to address was, of course, What was the most ubiquitous approach to energy management? However, how we could access a load and make it “smart” was where we started. At the time, we posed the scenario as such: We could either work to IP-instrument the world’s power distribution infrastructure, or we could simply look to extract the data on energy that is already there from within the various operating systems. As is often the case, Matt, John, I and were looking at the problem the same way but coming at it from different angles. Matt and John looked at the problem from the edge in and with POE in mind. I viewed it from the data center core out. In many ways, POE has been the gateway into network-based energy management, and voice as a service set some level of precedence for energy as a service. When treated as a service, both collapse functionality into the network that improves on the cost and performance over the current methodology (see Figure 1.23).
Figure 1.23 In POE deployments, the edge switching uses only one-third of its energy to route and switch data. A full two-thirds of the energy used by the switch goes to power distribution.
DATA 31% POE 69%
Cisco Efficiency Assurance Program, 2009
As we combined efforts across the far reaches of networking, we came together on some key goals in the initial build-out of EnergyWise: •u Use network switches as a unit of power aggregation •u Increase the span of control of the switch over power •u Provide time-of-day controls •u Manage a community of switches as a domain •u Realize a network effect of energy management at scale
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•u Don’t use existing network management constructs but advance network management •u Allow the network to be the command and control plane •u Have no single point of control or failure •u Stay agnostic, subscribe to no technology “religion” •u Ensure that every asset/entity that draws power is managed over the lowest common
denominator We started working together in our spare time to address challenges related to what I like to call the three Ms for the three Rs. This stands for measurement, monitoring, and management for reducing, reusing, and recycling or, said another way, Green IT. We divided the workload to suit our backgrounds, and we pulled in a lot of help from our peers. What came out of this effort was a game changer. If we assume that going directly to the operating systems of hardware is the least expensive and most backward-compatible approach, then it becomes clear that the network is the right choice as an energy management platform. In addition, the network can mitigate many of the proprietary barriers that have been built up over the years. So if the network is the right place for the “smarts,” who has the most network infrastructure deployed globally? By some accounts, 70 percent of the World Wide Web runs on a Cisco IP backbone. Although energy was somewhat peripheral to Cisco’s core focus at the time, we were lucky to have the support of our leadership to integrate energy into the stack of network-manageable attributes. Although it sounds simple, it was a big challenge, and what John Parello and his team were able to deliver was truly special. Not only did they provide several improvements on existing network management capabilities, they were able to put in critical risk-mitigation algorithms that let managed entities trace dependency paths (see Figure 1.24). Another point that he and Matt successfully advocated was to keep Cisco EnergyWise open and free—open meaning the Application Protocol Interface (API) is exposed, and free meaning that EnergyWise is simply a feature set within Cisco IOS.
Figure 1.24 Energy control needs to account for risk mitigation. EnergyWise entities are aware of one another’s criticality levels, ensuring both power and data availability across the network. Cisco Systems, 2009
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Smart Grid And God said, “Let there be light” and there was light, but the Electricity Board said He would have to wait until Thursday to be connected. Spike Milligan The idea of “smarter” grids is to provide improved monitoring and management tools for existing electrical grids. The current state of management for the world’s power grids is antiquated when compared to what is used in the IT industry. Grids in the United States, Europe, and much of Asia were built over the past 100 years or so, and they started to be networked in the 1950s and ’60’s using a SCADA methodology and protocol. This approach is very limited as compared to an IP-based network. It is also less secure than an IP network by most accounts. To summarize, we have a long way to go before we have smart grids, but the drivers are here and the investment has begun. There are multiple drivers for a move toward smarter grids. None of these drivers have been enough individually to motivate large utility providers to invest in IT systems to optimize demand response. Utility demand response (UDR) is similar to the IT notion of capacity planning. It involves forecasting energy requirements, price setting, and distribution management. The current state of demand response provides very little data on what losses are experienced through distribution. The current system is also ill-equipped to deal with local generation sell back to the grid. What appears to be pushing utilities past the tipping point of adopting IT systems is the combination of increased fuel costs, security, local generation, and plug-in automobiles. Some large utility providers are in the pilot phases of assessing IP-based monitoring and management elements for their demand response. PG&E is deploying digital utility meters in target markets in California, Duke Energy is advocating that the U.S. federal government support smart grid investment, and many others are looking for guidance in this space. Although the smart grid is still just a concept, some progress is being made on IP-enabling electrical grids. General Electric Company (GE), IBM, Cisco, Silver Spring Networks, and many others have already started marketing their technology. The full implications of a smart grid are many, and they are more appropriate for a separate book. However, you should keep some key points in mind. A grid involves a user and a supplier, so having capabilities in your energy management approach for inputs from a utility will be a priority. Ideally, you will want to have a system that has a high degree of automation and changes your organization’s electrical consumption based on key triggers. Some of these triggers might include the following: Capacity Alerts As grids continue to be pushed to their limits, rolling blackouts will result. Having a load-shedding strategy for your operation in times of tight capacity will help mitigate risk, and such a strategy might be a requirement to be eligible for certain utility and governmental incentives in a smart grid world. Cost Most data centers are not on time-of-day billing rates from utilities—that is, they pay a relatively fixed cost for a period of years. If this begins to change, there could be significant cost exposure to the user. Correlating your consumption profiles with time-of-day costs could reap additional savings while protecting from cost volatility. Some users already do this type of load management for bandwidth purposes, running batch processes at night when traffic is low.
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Access and Data Historian Currently, the electrical bill is treated much like a credit card statement; it gets reviewed monthly, and providing there are no major changes, it gets paid. However, there is no system to let you know if something has gone wrong—for example, when a credit card is stolen. Developing granularity into your electrical systems gives that level of real-time oversight. Having a business-to-business plug-in with your utility for tracking, reporting, and paying for your energy helps achieves this. Equivalencies Having plug-ins with your utility provider to track energy use could also be used to track carbon dioxide equivalencies (CO2e’s). Whether a utility provider delivers this as a service or a governmental department of energy does, it may be the norm in the near future. This is the most obvious example of data set conversion, but there will be other areas in-house such as thermal energy to electrical cost. Governmental Reporting Whether it’s the consumer or the energy provider that will be responsible for reporting their electrical use and greenhouse gas (GHG) remains to be seen. However, an attribute of your energy management program for which you should be prepared is to begin reporting some environmental data directly to regulatory agencies.
Flex Days In California, flex day alerts are issued from utility providers to the largest consumers. These alerts communicate to corporate and government users that the grid in their area is within 10 percent of full capacity. When 100 percent is approached, the rolling blackouts begin soon thereafter. These alerts have declined in recent years primarily because of weather conditions and more locallygenerated capacity coming online, but the risk remains. A useful exercise is to call your local utility provider and inquire about your region’s capacity profile and cost structures for billing.
Local Generation Photovoltaic (PV) and turbine technologies are improving rapidly but are not to the point where government or large enterprises are investing heavily in these technologies. There are some high-profile exceptions, however. In 2007, Google began deploying a large PV installation and has since reported that their PV installation has provided more than 7 percent of the total energy capacity requirement for their global headquarters. A visit to this installation in Mountain View, California, gives you a sense of how much space it takes to harness that capacity by using our current solar technology. The parking lots, the roofs, and anything else that has a good mounting surface are covered with solar panels. However, it is a laudable effort, and as PV efficiency improves, capacity capabilities will as well. Given the capacity constraints of locally sited wind and solar installations, large energy-consuming businesses will be reliant on the electrical grid for decades. However, with the capacity that local generation can deliver, a portion of that energy can be stored using fuel cells. It is foreseeable that, in the near future, energy management capabilities could be designed to shed noncritical load and use fuel cells to support key data center zones. If this is replicated globally,
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The Future of Energy Management 39
it could provide different energy-supply options. Using virtual machine mobility coupled with energy management could give data center operators the option of changing physical locations that host critical services with little to no disruption. This would provide a level of grid independence, meaning that you would have multiple power-supply options. As the grid evolves, it will be possible to sell back locally generated energy provided that storage options are available. Fuel-cell development shows some promise to address storage issues, but the reality today is that on-site renewable generation has not proved commercially viable for broad-based adoption. However, if local generation develops significantly, there will be an increased need to manage, measure, and automate aspects of the electrons it generates. Wireless and Internet protocols appear to be the logical choice, so there is much work to be done by the IT industry to develop solutions. However, this will happen only after the economic drivers and governmental support are in place. Given that this book is focused on the consumption elements of energy management, we opted to limit the generation discussion to what your system should be ready to interface with in the next few years. For now, the “grid” is entering the “first grade,” and we are all very proud. There is much work to be done and much opportunity for those who have a skill set in energy management. After all, whatever development is done on load-side management will need to interface directly with the consuming or “business” side. This is why the work being done today on smart loads will lead to smarter grids. The term micro-grids we mentioned earlier refers to power distribution within a consumptive context such as a building or campus. Local generation, smart grids, carbon, and energy management will all be inextricably linked from this point forward. The team you build will need to understand what entities it needs to manage now that the IP-based management technologies are here.
Energy Security Renewable resources are already being looked at as a form of energy security. As conversion efficiencies of solar and wind improve, the return on investment will as well. As you develop your energy management program, look into the capacity per kWh costs of locally generated power. After you know the price points, you can look into a deployment plan for buildings that fit a certain profile. The profile that you develop can include what processes these buildings support. For mission-critical facilities such as data centers, local generation, and storage of electrical supply make good business sense.
Where Is Energy Management Headed? As of July 2010, Cisco’s network-based approach is still the most pervasive and backward-compatible approach in the industry. There is yet to be another approach that can provide the same scale of savings at the same level of capital expenditure. The work we started with EnergyWise was the starting point for this book, and the move toward intelligent, automated energy management is on the horizon. Since its inception, we have been busy getting the IOS code that runs EnergyWise established as a feature set across the many product lines that Cisco provides while working with partners to embed EnergyWise agents into their operating systems.
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In addition to scaling this approach to energy management internally, Cisco provides a developer’s network; the Cisco Developer Network (CDN) allows other manufacturers to access a software development kit (SDK) for Cisco EnergyWise. Just as Apple has done with the iPhone, Cisco is allowing developers to make their infrastructure EnergyWise-capable and develop applications on top of it. Although this is not needed for basic monitoring, a developer’s toolkit is needed for control of non-Cisco infrastructure. Some might argue that Cisco is not being completely open and free in this approach in that EnergyWise has to be run on a Cisco network. There may be some truth to this, but let us remember, however, that Cisco is a publicly traded company and needs to, at the very least, set preferences for its products. Some purists might disagree, but we would argue that it is laudable that Cisco would give many elements of EnergyWise away as part of a software package that is already deployed. As network-based capabilities evolve, we will better be able to manage energy use, fuel sources, and use-case prioritization. The network is becoming the platform of choice to monitor and control energy use. How this plays out remains to be seen, but clearly a further blurring of lines will need to occur between facilities and IT. The drivers appear to exist already, but the technology, organizational structure, and processes need more development to be replicable on a broad basis. We discuss in later chapters what can and cannot be done today. This will set the foundation for you to be at the forefront of this potentially lucrative and professionally differentiating field.
Bringing Building and IT Networks Together In addition to developing native energy-management capabilities for IT infrastructure, Cisco invested in technology to manage facilities. In January 2009, Cisco announced the acquisition of Richards-Zeta (RZ). RZ had been providing a compelling building management system to an IP-network mediator. This mediator provided protocol conversion and gateway-like access into facilities infrastructure. Now known as the Cisco Building Mediator, this stand-alone unit allows you to extend an EnergyWise management construct into a range of facilities equipment (see Figure 1.25). Although this is still an area in development, there are sizeable deployments of these mediators for remote monitoring and protocol conversion purposes. Having mediation capabilities that also provide control has the potential to nearly double the savings you achieve through managing IT infrastructure. While more development is underway, building mediation and the fact that many facilities components like UPS, computer room air conditioning (CRAC), and power distribution units (PDUs) ship with a network interface card (NIC) contribute to IP-based management. The building management industry has been slow to adopt Internet protocols completely. We may be at a tipping point for two simple reasons. The first is that the Internet Protocols are far and away the most common, open protocols on the planet. Because of this, the price points for IP-based interfaces are at an all-time low and the range of interoperability is very high. Although analog cabling is less expensive than Ethernet, wireless capabilities can now mitigate those costs. The second reason is that professionals entering the building and facilities workforce out of college in the past 10 years or so are the first in history to have grown up with personal computing. IP is the protocol with which the new generation is already familiar and has a strong predisposition for using. Cost, ease of interoperability, and familiarity when taken as a whole are painting the picture of a future in which building management systems and IP networks are natively conversant. Fully integrated building and IP networks may still be far off, but the move toward monitoring energy via IP will naturally drive us there.
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The Future of Energy Management 41
Figure 1.25 Building mediation enables a user to extend an active energy management framework into critical facilities infrastructure. Cisco Systems, 2009
Campus
Datacenter/NCC
Utility Network
Internet Edge MSP Network
Core
WAN Edge
Internet
Building A
Cisco Network building Mediator
Building B
Branch WAN
Automated Demand Response Flows to/From Utilities, Weather Service Feeds, etc. Management and/or Asynchronous Date export Flows Cite to Cite or Client iPoed VRI Connection PS485 (PZ-LAN)/RS232 Device/Port Integration I/O Selections BACnet/IP, Mocbus/TCP, etc. Device/Port Integration
Energy Mgmt. (North Side) VLAN Segents Energy Mgmt. Device (South Side) VLAN Segments Data/Voice/Other VLAN segments
Chapter 2
Benchmarking After you have a sense of the scope of considerations involved in establishing energy management, you can move on to gathering the raw data you will need. The recommendations in this chapter are intended to serve as a guide for building useful data sets. They are not intended to be a one-size-fits-all approach, because too much variation exists in the types of data available to do so. The tenets for the data will be primarily expressed in risk, cost, and time. In this chapter you will learn the following: •u What data sets will best support the case for energy management •u What functions in your organization will supply the raw data needed to build an energy
benchmark for your organization •u How to benchmark energy use across multiple business assets and functions •u How to structure your data to express benchmarks that can be measured against the im-
provements that will be made in your program
Understanding the Scope of Considerations There is much conjecture in the industry today on how much energy is used by IT on any given day. This is not surprising because the IT industry is a complex model of software applications and hardware platforms supported by mechanical, electrical, and plumbing infrastructure. Vendors battle every day to innovate and supply profitable solutions, which results in a constant state of change. The same phenomenon occurs with users of this infrastructure. Google often states that their data center design and operations are one of their key differentiators as a service provider. From an analytics standpoint, the result equates to trying to clock the speed of a 747 that is still being built while in mid-flight. To mitigate the spiraling analytic complexity of IT and facilities, be sure not to get sucked into conjectural discussions. Stick to the data and you will be more effectual and agile. The data available to you will vary depending on how your real estate and IT operations are managed today. You will, of course, be working toward improving this, but initially you will be reliant on what is already there. That being said, we cover how you might be able to use soft instrumentation at this state of data gathering in subsequent chapters. For the remainder of this section, we approach the issue of benchmarking under the assumption that you have access to only the existing data that would normally be tracked by a large organization. The good news is that typically enough data is available to get executive support for an in-depth assessment that might include new instrumentation. You might face some basic challenges in getting access to this data. You will often get the question, Why do you want this?
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| Chapter 2 Benchmarking or To what end will this data be used? It’s important to keep it simple at this point. You might have high hopes and a grand vision for a full energy management program, but now is not the time. To get your program off the ground, you need to establish some level of benchmarking on energy usage. We had a standard, guiding statement for each phase of the project. At this point, we said the following: We are after basic data sets related to energy usage with the intent of determining what savings are possible using the current scope of energy management technologies.
Understanding Traditional Accounting Frameworks There are many challenges to overcome in building an energy management program, and the largest organizational challenge is bringing Facilities and IT together. Generally speaking, IT operations do not directly pay the power bill. Large organizations that do nothing but data center services are the exception to this rule. Search engine providers, file hosting, web multimedia, and the like are often more integrated. You will discover that there are many differences, but try to be the least disruptive to the current system while still achieving your goals wherever possible. Because each organization is slightly different in how a Facilities department “bills back” electrical use to occupants, we will focus on some fairly common examples. You will be working on improving your system, so it’s important to know how electrical costs are currently allocated. Occupancy-Based Systems These systems basically look at butts in seats. Cube farms, call centers, and trading floors typically have a fairly high degree of standardization in the infrastructure that a person would use—phones, lights, desktops, laptops, and so on. In this scenario, many facilities-management approaches can estimate the power required for each worker. Then they bill back business units for their occupancy usage. The main variations in an occupancy-based system come from factors such as lighting, air conditioning, and laptop usage. The rest of the IT infrastructure, such as IP phones, desktops, printers, and wireless access points, draw electricity at a relatively constant rate. Geospatial Power-Allocation Systems These systems are more often used for lab and data center environments than for office space. This approach basically fixes a cost when a new facility is constructed and is built to a kW-per-square-foot target. This fixed cost translates into a rate that is billed through a provisioned wattage per a given floor area (kW/ft2). It is common to see data center environments that draw 100–200 watts per square foot and that range from 10–15,000 total square feet. Using an example of a 10,000-square-foot facility at 100 watts per square foot at $0.05 kilowatts per hour (kWh) would equate to an annual power bill of $438,000. (See the formula in the “Energy Accounting” section of Chapter 1, “A Stake in the Ground.”) This method doesn’t always lend itself well to mixed-use facilities, but it is widely used for data centers and data center hosting. Submetering Systems Submetering systems are ideal in that they give you the best level of granularity for specific electrical usage. Just as it sounds, a submeter is a basic electrical meter that can report back through a building management system at the branch circuit level. Any of the big electrical vendors— Siemens, Schneider Electric, and Johnson Controls—all provide options for granular energy monitoring. If you are lucky enough to have this today, it will be a great addition to your program to get access to this data. Submeters coupled with IP-based energy monitoring will give you the ability to separate facilities from IT energy usage. This will be particularly useful later when you structure your cost allocations.
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Understanding the Scope of Considerations 45
Although more approaches certainly exist than these three, and often hybrid systems incorporate different elements of each, these are the usual suspects. Typically, a high degree of variance occurs in the complexity of these approaches. Some systems are very simple and charge a flat rate. On the other extreme, others are so complex that they don’t scale well. In general, the value your program initially brings is to complement the current approach through improved instrumentation. Over time, your program might completely replace the current system, but be careful in how you position that up front. There are bound to be some people in your organization who feel personally invested in the current system. For these people, mitigate any perception of disruption to what they are doing today.
Use What Works Providing you keep your goals clear and maintain a collaborative posture, you should be able to get access to some basic data sets from your Facilities and IT teams. These data sets can serve as the foundation for many accounting models, which can be expressed within the context of the current operation. Keep in mind that the same teams with whom you will interface throughout your data-gathering efforts will in large part be the stakeholders for a program you are seeking to build. Don’t miss the opportunity to brief them on your plan to save the organization money and seek their input on the operative realities that exist. Generally speaking, the data sets you gather for an initial benchmarking activity already exist. How you structure them is what will be unique to the organization and what we cover in this and subsequent sections. The best places to start mining information are existing asset and inventory lists. There is a lot of variance in exactly what is tracked, but an electrical requirement is almost always included. Figure 2.1 shows an example of a typical IT asset inventory list. Power is often included in these lists based on the vendor specification from a data sheet. We discuss the differences between specified power and actual draw later in this section, but your total capacity benchmarks can usually be set based on your existing asset lists.
Figure 2.1 Typical IT asset and inventory list Inventory Plus
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| Chapter 2 Benchmarking To frame your thinking around the analytics, make sure you become familiar with the foundational metrics in the following list. Do more self-study on each; Wikipedia is a good central location to get started. The following descriptions are high level and in a business context so that you can explain them as integrated metrics in your program: Watts Among other things, watts indicate a rate of energy use over a period of time. It is easy to find out the wattage requirement for just about any IT or Facilities asset in your inventory. Data sheets list the watts required for the power supplies of a product. You can also arrive at watts by simply multiplying volts and amps. In most cases, the power supplies being used today are power-factor corrected, meaning that volts × amps = watts. Watts per hour will serve as the foundation for most of your financial business case. Many other data sets include this core metric. Volts You will find that there is some variation in the ranges of voltage globally. However, volts will be a subset of watts in your management model, as watts include voltage. As you look into the dynamics of how we move electrons around, you will find that higher-voltage distribution often works more efficiently, because fewer step-down transformers are needed to support it. Fewer transformers mean fewer losses through conversion of electricity. As electricity is converted (via transformers) to lower voltages, some of the electrons escape as heat, meaning you lose some electrical capacity as thermal energy. Finally, higher-voltage distribution enables you to deliver more power over the same-gauge wire (less copper), and power supplies in the data center infrastructure can accept a wide range of voltages. Amps If you are in IT, you might be surprised to learn that your Facilities department uses primarily amps for sizing. Facilities professionals understand many forms of energy mathematics, but usually size is indicated in amps for one simple reason: It’s amperage that determines the lethality of contact. Think of amps in terms of bandwidth but with a constant state of high-volume network traffic. In electrical terms, this traffic would be the voltage, and the amperage would be the transfer potential of the data cable. So if the transfer is constant, the potential data you could receive depends on how many gigabits can be supported. This is why you will see a clear difference in physical size of power cables for different amperage. A 100-amp power cable is about the thickness of a garden hose and at 415 volts (V) can easily kill a healthy adult. This might explain why your facilities teams appear conservative as compared to your IT operation; their “network architecture” has the potential to kill people if mistakes are made. Energy Profiles Across IT and Facilities infrastructure, a simple power bell curve can be plotted after monitoring instrumentation is deployed. You will find that these bell curves mirror real estate occupancy for office spaces, but for most lab and data centers, these curves show a more constant state of electrical consumption. There is some variation in lab and data center loads, but less than what you see in office spaces. The variance in data center and lab environments’ electrical consumption is more closely tied to the software applications being supported. Some of these applications like email and web access can be very hard to align as they are happening all the time. Another example is batch processing runs for certain applications that are can be scheduled during hours when occupancy-based loads are low. So energy profiles will need to be developed for the infrastructure that supports your operations. We discuss how to structure and analyze energy profiles in later sections.
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Understanding the Scope of Considerations 47
These are the basic data sets that will enable you to express reductions in total energy use through the strategic application of available technologies. The savings you achieve by simply shutting things off will be expressed in kWh. This kWh structure is how your utility bills you for your electricity use today. Your costs can be estimated using the EPA’s Power Profiler (just conduct a Google search of EPA’s Power Profiler) but should be qualified through your real estate teams. Large organizations typically negotiate lower rates than what the EPA site reports, so make sure to check with your Facilities management team to qualify your estimates.
Power Data In general, the scope of power data is very manageable. The vast majority of the IT equipment you analyze is designed to accommodate a range of voltages. Common regional requirements are shown in Figure 2.2. Consequently, you will have no more than three main voltage sets to analyze (100/120 V, 208/240 V, and 415/480 V). Although still emerging in the United States, 230/400 V is an option to look into as well. Some data center operators in the United States are adopting 400 V distribution directly to the racks, including both UPS and transformers of the same voltage. Wattage requirements vary greatly by type of infrastructure, but for the most part they can be found in asset/inventory lists or on the Web. Ultimately, you are after watts because they will include voltage, amperage, and time. The only other critical metric to add to this is cost per watthour. With these basic metrics, you can establish a benchmark for power use, cost, and emission equivalencies.
Figure 2.2 Regional voltage requirements don’t vary much thanks to autosensing power supplies found in almost all IT infrastructures today. Wikipedia Commons
Tip You don’t need to be overly granular in your power data estimation. Unless you plan to become a utility company and provide digital meters, you are after data that is generally accurate. A 5–7 percent variance is more than acceptable at this point in your analysis; you will be providing real-time instrumentation options in later phases of your program. Often people are confused about the concept of nominal power draw versus a vendor’s published wattage requirement for a piece of equipment. This vendor-published electrical
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| Chapter 2 Benchmarking requirement is typically called the nameplate power draw. Think of the nameplate draw in terms of a pitcher of water. Let’s say the total amount of water the pitcher holds is 1 gallon before it spills over the top. In practice, you would not fill the water pitcher to the point of overflow, because carrying it from the sink to the table would be messy or very slow. Instead, you would fill it with perhaps 0.7 gallons to make it easier to move. In this analogy, the nameplate power draw of a power-consuming component is the total capacity of the pitcher. So the nameplate power is the maximum electrical capacity that a component can support under electrical load testing. The nominal power draw, or actual draw of a component, is less than the published nameplate capacity and better reflects the operative conditions of the particular workload that a component supports. It is important to recognize the difference between nameplate and nominal draw. For a switched-mode power supply (SMPS), the predominant supply found in IT operations, the actual draw of the equipment is roughly 30 percent less than the nameplate. Said another way, if a vendor’s data sheet tells you the equipment’s power supply requires a total of 1,000 watts (W), the reality is that equipment will require roughly only 700 W under normal circumstances to do its job. Figure 2.3 provides an example of a power calculator we developed for our program that provides nominal power draws for data center equipment.
Figure 2.3 Some product efficiency calculators enable you to estimate power by using nominal draw versus nameplate ratings. Cisco Systems
As a workload varies (email traffic, web traffic, batch processing, and so forth), so does the actual draw. For this reason, using a 30 percent off the top approach will take you only so far. This approach works fine for the sake of normalizing your benchmark data, but there will be a clear difference after you instrument. This delta will come from the workloads that run across the different classes of IT infrastructure in support of different application workloads. Computing, network, and storage all have different characteristics in support of different workloads. Each domain also defines workloads in different ways. This lack of standardization points to the complexity of managing IT’s energy use without a common, standardized platform. We describe how the network-based approach we pioneered solves many, but not all, of these issues in later sections.
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Understanding the Scope of Considerations 49
Cooling Data Although power technically refers to the energy potential of something like dynamite, in common parlance it is often used to describe electrical requirements. In this book, we have opted to keep the language common, as you will be working primarily with business people, not scientists. Cooling can be measured in many ways, and the method you use to quantify it depends on what you want to know. British thermal units (Btu), for example, tell you how energy requirements change for air conditioning systems at different barometric pressures and dew points. Btu, joules (J), and computational fluid dynamics (CFD) modeling are all used in the cooling industry but are far more detailed than what you will need to establish benchmarks. Whether it is the electrical requirements for the IT and facilities loads that need to be cooled or those of the air conditioning units themselves, they can all be measured in watts. Watts are your best friend and will be the foundation of almost everything your program does from a business case standpoint. In the case of cooling systems, a basic wattage-based ratio can be applied. We describe how they can be instrumented in later sections. For the sake of benchmarking, we kept it simple and used a ratio approach we learned from American Power Conversion (APC) called a cooling burden factor. This wattage-based ratio can be used to estimate the corollary savings you will see from cooling systems as you reduce the IT load: Cooling Burden Factors This factor is a simple ratio of production power to cooling power. For example, if a server requires 100 watts to serve data (production power) and it takes an air conditioning system 200 watts to cool that one server (cooling power), you would have a cooling burden factor of 2. The formula is expressed as follows: cooling burden factor = production power : cooling power Applying a Burden Factor To determine how much electricity a cooling system uses, you will also need to do some basic asset association. Each computer room air conditioning (CRAC) unit is typically sized to support a radial or semi-radial area that correlates to the distance cool air is distributed from this unit (see Figure 2.4). In turn, each CRAC unit has what is typically called a throw. A throw refers to the direction and distance pattern to distribute the air that will be consumed by the IT infrastructure. This is usually no longer than 20–30 feet from the fans in the CRAC unit. It is often contained in a plenum space such as a raised floor or ducted overhead for distribution. The basic geospatial associations of IT assets to CRAC units are determined by their location, and throw is how you can estimate cooling costs across a data center. Within the racks, these cooling units enable you to detail the IT assets and associate them directly to a CRAC. This is how you can apply a burden factor down to an individual IT asset.
Carbon Dioxide Equivalencies When you determine the wattage requirements of your infrastructure and project a savings, you can convert that potential savings into an equivalent carbon dioxide savings. A carbon dioxide equivalency CO2e is a standard metric, useful in broadly converting energy savings into greenhouse gas (GHG) reductions. Simply take your total wattage and find the right conversion factor for your region on the EPA’s eGRID website (www.epa.gov/cleanenergy/energy-resources/egrid/index.html). This site will be a valuable resource for you, so make sure to bookmark it. The data you enter will yield a number that is reflective of CO2 intensity (expressed in grams of CO2) for a regional average of fuel sources. This makes it simple to take your total wattage and convert it into grams of CO2.
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| Chapter 2 Benchmarking Figure 2.4 CRAC units are sized by your Facilities operation and take into account power requirements, heat removal capabilities, and geospatial distribution of cool air.
Keep in mind that a reduction in wattage will almost always equate to a reduction in CO2 based on our current global fuel mix. The one exception to this rule involves a relocation to or change in the type of power that is used. The takeaway is that, when you have your watts, you can convert them easily into any of the cars on the road or trees planted examples you want. As stated earlier, CO2 and environmental considerations might not be relevant in the early phases of your program development. However, there are exceptions to this rule, and you might be lucky enough to have executives who have a Green vein. Our greatest successes in securing executive support for our program at Cisco and also with early adopters were achieved by focusing on the following: •u Business relevance in terms of risk, cost, and time •u Alignment to an existing corporate Green initiative •u Democratic innovation, through involving multiple stakeholder groups
Note As energy management applications and reporting evolve and carbon reporting becomes the norm, we might see a major change in how the private sector reports to governmental regulatory bodies. When Cisco released EnergyWise in January 2009, it provided an open client protocol and open management API. This hints at the ability to start automating what is today a very manual process—corporate and civic energy and carbon accounting. Any efforts to manage and report on CO2 will need open and standardized tools that can scale better than what we have now. In this regard, energy management and CO2 management will be directly tied to each other.
Accessing Benchmark Data With simple data sets of watts (electricity and time) and cost in mind, it is time to develop a framework on top of these key metrics. Building this framework is a necessary step in order to articulate your analytic methodology. You will need to demonstrate this methodology in many cases simply to gain access to these data sets.
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Accessing Benchmark Data 51
If you already have a close working relationship or are a member of a Facilities team, the next section might be a little redundant. However, we do cover some tips on presenting your initial framework that might be of interest. Either way, it is important to remember that being able to articulate the how and why of the data you are requesting will be important to gain access to this potentially sensitive data.
Getting Permission Be careful to keep your requests and your motives simple. If you don’t already have the internal branding as an energy expert, you can point to your knowledge of the technology from a costsavings perspective. The case study at the end of this book is intended to enhance your credibility in taking the first step toward building a business case for this program. Multimillion-dollar cost savings with little up-front capital expenditure is compelling. You can hold up the case study at the end of this book as an example worth striving for. However, for your initial data requests, it is better to describe your efforts as a simple fact-finding mission with the intent of determining an order-of-magnitude savings. Taking this bottom-up approach of working with your counterparts will serve you well in establishing your initial benchmarks. Of course, you will eventually need to articulate a vision, but in due time and to the right executives. Much of your success in building the relationships necessary to benchmark and ultimately develop a program will hinge upon your ability to show real value. Showing this value in a context that is easily understood is just good form. To that end, we did a lot of research on the job functions with which we knew we needed to engage before requesting access to their sensitive data. Our value proposition was simple: We will demonstrate how much money can be saved by implementing energy management… there are new technologies available that are compelling.
Presenting Your Value Proposition This is where it gets fun. By now you have a good sense of what you want to answer: How much can we save, what technology will get us there, and how do we do it? Be careful not to fall into the carbon-crying game by focusing on just CO2 and GHG as you approach stakeholders. Do your homework to understand your stakeholders and determine whether you should address the conversion of watts into other data sets later on. This will ensure that you appeal to the lowest common denominator for any business—money. Amazingly, there is still some debate on whether global warming is real, so assume that CO2 and GHG are out of a business context for some of your stakeholder groups. Adding environmental data later in a Do you want fries with that? manner will build your credibility among IT, Facilities, and Corporate Accounting teams. Although this is more of a social phenomenon and out of scope for this book, it is worth mentioning. Wherever you go in the organization, you will need to have a reasonably modular value proposition, which can speak to varied interests. To that end, make sure you spend the time to develop a good modular slide deck. The one we developed for use at Cisco was kept to fewer than 100 Microsoft PowerPoint slides. This might be all you need in an organization to get a pilot off the ground. However, be prepared to fully document your approach toward energy management. Whether you aspire to own this program and lead an internal IT-Facilities energy team is up to you, but project and program descriptions are a must. We cover what
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| Chapter 2 Benchmarking documentation is needed and when throughout each subsequent chapter. At this initial stage of framing a pilot, you should be working on a value proposition that covers the following: Vision What is your vision, and how does it align to any existing corporate-level initiatives and general business interests? Cost Savings How much can be saved and across what assets and operations? Is your view on cost savings broad enough to anticipate total costs across different hardware platforms? Technology Strategy What hardware and software platforms fit the operative realities of the current operations? Organizational Impacts How is the current organization prepared to adopt energy management? How deep you decide to take your value proposition throughout your program development and pilot deployment will be a fundamental question you need to answer and re-answer. This is an ongoing question, as you will constantly be evaluating and reevaluating your progress against savings. In our case, we worked for a major IT vendor, so the expectations were high. However, in the spirit of using what works, you might decide you don’t need to go as deep. We cover more on this in the next chapter as part of assessing the value of the data you collect. For your baseline assessment or benchmark of the company’s energy use, a simple but extensible framework is needed. Table 2.1 shows the same basic top-level framework we used at Cisco to present our value proposition. This high-level roll-up of electrical, spatial, and trending data sets enables you to set an aggregate point that can be aligned to IT operations. What starts to emerge through your baseline analysis are the moving pieces of what an energy management program will entail.
Table 2.1:
The top-level data framework
Total Electrical Utilization This field shows the total electrical capacity currently available to your organization. This includes a roll-up of facilities, IT, and real estate energy estimates. This field can typically be determined by mining utility bills or getting primarymeter readings from buildings.
Utilized Capacity (kW)
Total Capacity (kW)
Average $/ kWh
This field shows data coming from actual instrumentation or from tested and measured draws of infrastructure. This field will change dramatically as you bring new instrumentation options online.
This field is simply the raw kW data that factors into “Total Electrical Utilization.” It is important to represent this as a percentage because your program in general focuses on percentages, not absolutes.
This field enables you to aggregate and then average out all the building energy costs for your organization. Be sure to qualify your kWh cost estimates against actual billing data.
Annual Electrical Cost This field represents the total electrical cost to the organization today. This is the foundation of your business case benchmarking.
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Table 2.1:
The top-level data framework (continued)
Capacity Utilization Example
Utilized Capacity (kW) Example
Total Capacity (kW) Example
Average Cost Example
Annual Cost Example
75%
3,117
4,239
$0.08
$2,040,448
As simple as you might try to keep your initial scoping, scope creep always occurs. You will face questions of all sorts that can slow you down but are necessary to address to retain your credibility. Be prepared to discuss the process flow of your project and its overall organizational impact. The scope of organizational impact can be addressed by using a simple interdependence diagram, as shown in Figure 2.5.
Figure 2.5
Corp Green
Mapping your organizational interfaces builds credibility.
Chief Financial Office Operations IT Department
Finance Energy Management Team
Facilities Department
IT Infra and Applications
Consulting Engineering Asset Management
Capacity Management
IT and Facilities Vendors
If you work within an IT operation already, you should look into what asset information is currently tracked. You might find that the data you will collect on power and cooling can already be aligned to other areas. Most large IT operations are already tracking computing and storage growth against business growth. If you can gain access to some of these growth figures, you can contrast them against power, cooling, and spatial capacities. This crude re-creation of data center capacity planning is appropriate at this stage of your program development. After all, you’re interested in orders-of-magnitude savings, not hard figures. Figure 2.6 shows an example of how aligning growth percentages can provide highly strategic information to your stakeholders. To close out your initial value proposition of gaining access to benchmarking data, make clear that it is for funding and support of a proof-of-concept (POC) deployment. This POC happens only if the numbers look good, and a pilot deployment happens only if the POC looks good. Taking this iterative approach will help you limit the natural scope creep that comes along with a project like this. From there you call out what phases will lead up to a full deployment, as seen in Figure 2.7.
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| Chapter 2 Benchmarking Figure 2.6 Aligned capacity analysis is simple but effective for presenting your value proposition.
150% Cooling Utilization Total Available Capacity
100%
Electrical Utilization
50%
0%
Figure 2.7 Phases of deployment coming from your project planning will dictate the activities required to build the program.
Today
1 Data Discovery & Financial Model
Year 1
Year 2
Year 3
2 Codify Development & Deployment Plan
Year 4
Year 5
3 Deployment and Service Developent
Goals
Goals
Goals
Activities
Activities
Activities
’ Q1
Q1
Q1
The best way to make a project like this successful without being directed by executives to do it is through iterative checkpoints with clear success metrics. Finally, it is important to call out that some of the assumptions you are making for the initial business case will be answered precisely through the pilot effort. Some examples of assumptions you will clarify through your pilot include the following: Accuracy of Existing Data It is likely that a large portion of the existing data your company tracks on energy use has been built on estimations from one or several teams. This is primarily due to the lack of instrumentation that is deployed or the inference that is employed across instrumentation already deployed. Growth Projections Growth projections are a challenge for any large IT operation, and power plays a pivotal role. Where possible, try to align your projected savings to business growth, but call out these projections as dependent on the business growth projections typically coming from a Finance team. Assumptions Inherent to Existing Billing Systems In many situations, you will need to break energy use down into an infrastructure domain and departmental billing construct. Be sure to research and understand what assumptions go into the current billing systems.
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Inaccuracies in Software-Based Power Reporting Most IT assets do not have instrumentation on the power supplies within the chassis that consume, convert, and distribute power. Some assets monitor the direct current (DC) bus amperage while others simply perform a table lookup when queried through an operating system. In our experience, we never saw more than a 7 percent variation, and most reporting methods were well under a 5 percent variation. For the sake of benchmarking, it is fine to have a 5–7 percent variation but important to call out that delta in your assumptions.
Energy Allocation by Asset Type Initially, you will be making simple estimates of energy use by hardware platforms, as shown in Figure 2.8. If you are lucky, your IT asset inventory lists will have a completed field for power use by asset. Even when this is included, it is typically a nameplate power figure (coming from a data sheet). One significant value of your pilot is to determine this number with a much higher degree of accuracy. Through the discovery phase of the pilot, you will “autodiscover” asset-level energy use based on how it is reported by each operating system. Determining power use by asset is described in greater detail in later chapters. This level of central asset discovery in and of itself is a big value, and you should make sure to highlight that in your communications.
Figure 2.8
Average Building Energy Profile
At the highest level, you want to show by percentage how much electricity is used by IT assets only.
IT Infrastructure 40% HVAC 35%
Other 10% Lighting 15%
Tip If you do have an asset inventory list with the power field filled in as a standard practice, make sure to find out the origin of these figures. In our case, Cisco had an asset inventory that relied on user inputs the day an asset was registered for network connectivity. Because the power figures were an optional field, many were not filled in. For the assets that did have power data, it was taken from the data sheet for a product. As covered in the “Power Data” section of this chapter, you can normalize this data for IT infrastructure simply by taking 30 percent off the top of the data sheet figures for estimation purposes.
Energy Allocation by Platform For both hardware and software, you will want to roll assets into a series of aligned bubble charts, as shown in Figure 2.9. These bubble charts help you focus your message on where the
| Chapter 2 Benchmarking real work will be done to achieve savings. This framework also enables you to provide a basic road map for technology assessment and organizational impact. Finally, it will be useful in mapping business applications to infrastructures at a later date. Electrical Cost by Hardware Platform
Figure 2.9 Showing your organization’s energy usage down to the platform level is an important step in prioritizing your specific approach.
$4 Annual Electrical Cost* (Millions USD)
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$3
$2
$1
$0
0
2
4
6
8
10
12
14
16
18
Total IT Units Clariion Symmetrix NS-400 Thunder PowerEdge Niagra x86 UX
UCS CM Route Switch Sybex Syber Switch WS400
*Does not include cooling costs
As you begin to map application dependencies (or reference them if already available), you can also build a checklist of software options. Many large IT vendors provide a power management solution for major software platforms that are hardware specific. Examples of different power management applications are referenced in later sections. If some of these platforms already have these applications deployed, you can access these for specific details on the current state of electrical usage, platform by platform.
Note You should/must have a central repository for hardware and software linkages before deploying any broad energy management. You need to know what applications will be affected by what infrastructure before you plan to shut it down. For this reason alone, you should consider pushing mission-critical data center environments to the final phases of production deployment.
Where to Get It Both Facilities and IT operations maintain their own asset lists and, in many cases, with no standardization between them. We determined that by targeting the most power-dense environments (data centers and labs), we could get the most data within a relevant, predefined context.
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People tend to understand centralized energy use better than distributed. More standardization and better tracking of assets occurs in these environments. We opted to target the IT infrastructure before the facilities equipment simply because, in data-center-type environments, facilities are built in proportion to IT requirements. With that in mind, we will show you how to use some basic ratios to estimate how changes to IT infrastructure will impact facilities’ electrical use. At this stage, you will get your baseline data primarily across three organizations: Facilities, IT, and Corporate Accounting (Finance). Because billing and asset management between Facilities and IT is typically done separately, you may be able to approach the CFO for a roll-up of both.
Real Estate and Facilities Departments Depending on various factors, a well-organized real estate operation can be your one-stop shop to establish your business case. Many large corporations often have fairly accurate tracking of energy use. If you’re lucky, the Facilities department will have the following: Raw Electrical Costs across the Enterprise There should be a role in your organization that tracks utility costs centrally. This individual or team will most likely manage the company’s utilities contracts. This usually includes negotiating with utilities globally to provide basic electrical service.
Note Not all companies are comfortable or contractually able to publicly expose information about their energy usage. Make sure to approach this data and the individuals who manage it with security and diplomacy in mind. Conversely, your company might already be reporting on sites such as the ones we mentioned in the preceding chapter (for example, www.openeco.org). Building Electrical Usage A roll-up of building power is almost always available by the way companies are billed for power. This provides a good level of detail to complement your asset/inventory approach. These buildings represent the total energy use in your analysis, and the IT assets will, in many cases, be the largest percentage therein.
Tip Building energy use is an excellent data set but might not be completely necessary at this stage. Remember, you are approaching your benchmarking effort from an IP management construct (IT assets), not a building-based one. You should concentrate on one at a time, and because facilities are built to support IT, focusing on the IT infrastructure first is the logical approach we took. Regional Fuel Mix Some organizations are already tracking their carbon footprint. Consider yourself lucky to work for a company that is doing this if you want to implement energy management. If a company is doing this, it already has a green thumb or two. Having other people in positions of influence who want to manage resources better will help on many levels. A function like this will typically express an estimation of the organization’s grams of carbon dioxide (g CO2) per kWh.
Tip Although Green monikers are a nice-to-have, focusing too much on CO2 levels can draw much
needed attention and effort away from managing the energy you can today. Focusing on reducing wasted watts will be, by extension, a reduction in CO2. Think about the key individuals in your organization and consider when and if to bring CO2 into the mix as part of your benchmarking, POC, pilot, and deployment. CO2 can always be run as a parallel metric and, if tracked, will serve you well in opening your program up to specific Green incentive programs from utilities and through governmental initiatives as they become available.
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| Chapter 2 Benchmarking Other Nice-to-Haves In our case, our Cisco Workplace Resources (WPR) teams went so far as to track emissions from many areas of the company. Refrigerants, kitchen emissions, and even employee air travel were looked at by Cisco to provide a CO2e benchmark. Any company seeking to manage carbon will find a challenge in the scope of the problem and limited technology available to address it. This is where your value proposition to any such organization will be proven technology, primarily focused on managing energy. In our case, this approach simply complemented the great work already underway by our WPR teams.
Tip Many real estate departments are outsourced these days, so it may be a challenge to locate the right person. In these cases, you can look to the Finance department in your company to get what you need. Starting first with Facilities teams will give you better data in most cases, but when none exists, you can at least get energy costs from the Finance department. Facilities and real estate teams can be your best friends in this effort. Just make sure you understand the operative realities that exist for them. They are incented to be adverse to risk and they are engineers at their core, so make sure you have your value proposition clear before approaching them. We’ve seen many examples of real estate teams working with vendors to incorporate energy and carbon in performance-contracting models. These performance-contracting offerings are typically based on a percentage-of-savings, bill-back model. Structurally, this is similar to what you will build in your IP-based energy management program.
Finance Departments Depending on how deep you need to take your analysis for a pilot, your Finance department can also be a one-stop shop. Although the Finance employees won’t be able to provide the same level of detail as a Facilities operation, they can certainly get you the raw electrical costs you will need to get started. In our case, we needed more detail to get our pilot off the ground, but the expectations of executives in every company are different. If you can get away with showing straight electrical cost estimates across IT assets, working with your Finance team might be all you need. There are some data sets that you can get from the Finance department that can complement other data you might garner from Facilities and IT. As in the case of Facilities, this data is sensitive, and approaching it as such will go a long way toward obtaining it. We looked to Finance to provide the following data sets: Building-Occupancy Billing To get support from different business units, you might need to show each group’s leadership the percentage of total power that is currently allocated to their operation. Your value proposition is horizontal across business units, but this might be a qualifying question you’ll have to address in order to receive support. There are many approaches to billing for building occupancy, but most are focused on office space allocation per employee. For example, the sales function may require 60 out of 100 cubes on a floor. In this case, a crude 60 percent of office-space power can be used for a particular site related to sales. You can get away with this because typically a high degree of standardization exists in office ergonomics.
Note Be careful to call out when a data filter, such as building occupancy, is applied as an estimate only. Part of the value you bring through your benchmarking effort is best-case accuracy for energy data discovery today.
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Asset Procurement Basic capacity trending needs a metric to represent growth. Finance tracks spending on IT assets closely, and electrical growth projections can be made from that. Cooling requirements can then be inferred from there. These are loose affiliations but can serve to provide a growth metric where none currently exists. This growth metric is represented as a percentage of IT infrastructure growth in relation to business growth. You can certainly dive into more detail in profiling asset types by power, cost, and growth, but at this stage you shouldn’t have to do this. However, you should look into and understand how depreciation, taxes, and refresh practices influence asset management. This can be a parallel “plug-in” into your program later on. You might find that the Finance department within your organization is the best aggregate point for the data you need. Although you will need to get detailed electrical capacity figures from your real estate operation, that function may already be in Finance. Because you are targeting IT assets only for your pilot, costs per kWh and IT asset inventory lists might be all you need for a simple business case. What you do with the Finance and Real Estate teams will depend on how many cycles you have to crunch the data and what you want your program to do. Working with your Finance teams directly will help with many aspects of your program— not just in establishing the initial business case, but later when you address cost allocations. Because most Facilities teams are paying the bill for the energy that IT uses, some new planning is needed. We dive into this in greater depth later in the book but mention it now because your contacts in Finance will be with you throughout the many phases of your program. Building a good relationship here will pay dividends later.
Capacity Managers You might find an aggregate point for many key data sets through your capacity planning teams. Most often found in data-center-related operations, a capacity manager is responsible for forecasting and provisioning electrical, mechanical, and spatial capacities to an IT operation. This all happens with much advanced planning, as a new data center or branch office can take 24 months to build. In many cases, this function is made up of a small team or even an individual person for larger organizations. If your company has this function, make sure to build strong relationships with its staff because they may be able to save you time in your initial data gathering.
How We Did It: Capacity Planning Our teams worked with Andy Broer and Doug Alger of Cisco’s IT teams. Both Andy and Doug have a facilities background but are employed by an IT operation. Andy leads a capacity management team, and Doug is a senior architect. They were instrumental in helping us assess our program within data center terms. Much of the data and coaching they were able to share with us helped make our program a success. Could we have done it without their help? Maybe, but it would have taken us twice as long. If you have these functions in your organization, seek them out and build relationships early on. They want what you can build.
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| Chapter 2 Benchmarking Many elements of an energy management program are already evident within capacity management teams today. Because energy management at the enterprise IT scale is a completely new approach in the industry, you can look to data center capacity management models as a guide. If you do not have this function or would like to build it within your energy management program, we’ve provided some basic attributes of corporate-level capacity planning in Figure 2.10. We’ve also highlighted the data points you can gather from each area.
Figure 2.10 Capacity planning teams already cover many elements of energy provisioning and forecasting.
Process Overview: Capacity Management Processes ITIL Processes Outside the IT Outside Organization Service Design Customer Process
Service Design
Service Design Processes
Service Design Processes
ITIL Processes Processes Outside Outside the IT Service Design Organization
Service Strategy
Service Strategy
Service Transition
Service Transition
Service Operation Continual Service Improvement
Service Catalague Management Service-Level Management
Service Operation
Business Capacity Management
Business Capacity Management
Risk Management Availability Management IT Service Continuity Management
Continual Service Improvement
Service Capacity Management
Capacity Management Reporting
Service-Level Management Risk Management Availability Management IT Service Continuity Management
IT Security Management
IT Security Management
IT Architecture Management
IT Architecture Management
This very nuts-and-bolts responsibility must also look at business growth to predict when new construction is needed. Determining how IT and Facilities, by extension, will grow to support the business is often the most inexact part of this role. This is where you can bring value back to these teams after your program is in place. The instrumentation you deploy will provide close-to-real-time information on energy use at the IP-address level. As you can imagine, this can prove useful when contrasted to time-of-day workloads across the WAN. It can also be tracked and trended, giving your capacity planners a better data set to compare against business growth. At Cisco, we have worked with the capacity planning team within the Network Data Center Services team with great success. Headed by Andy Broer, this team has already built many foundational elements of energy management such as rack-based temperature and humidity monitoring, tested power draws for IT infrastructure, and a robust building management system. Working with Andy and his team saved us a lot of time initially in determining what assumptions were safe to make regarding Cisco Data Center electrical usage and criticality level. Figure 2.11 shows a roll-up of the data sets this team was already tracking for global data center operations. What you should have clear in your mind before engaging these teams is that risk aversion is their predominant concern. What we mean by this is that they have zero tolerance for downtime and would never risk system availability for a cost-cutting measure. However, cost is a close second, and these teams could be your strongest critic or your best ally, depending on how credible they find you to be. For this reason, it’s important to approach them with a humble posture and with shared learning in mind. Make sure you are positioning them as a major stakeholder in your program. They will likely be happy to act as your risk mitigation consultant for the program moving forward.
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Figure 2.11 An example of a capacity planning team template
Instrumentation Options For this section, we want to make the distinction between using existing instrumentation versus anything new that you will install later. At this point, you are targeting a benchmark and pilot framework only and shouldn’t need to include any new instrumentation. You are simply building an analytical model to assess the value of implementing an energy management program for your organization. Although you certainly can deploy new, no-cost instrumentation, you might be putting the cart before the horse. You want to get consensus on the financial elements of the analytical model before you approach the technological ones. There are several reasons you would look into current energy instrumentation as part of your benchmarking. First, it will educate you, to some extent, on the status of your organization today in terms of managing energy across IT and facilities. Second, it might help you to narrow the scope of your program. A perfect example is what we all hear about building energy management—that even accessing this data might be cost-prohibitive because building energy use is rarely networked (via IP). Third, if the instrumentation deployed is working well, then using it might save you time and money in filling in the blanks of your benchmarking effort. In the upcoming sections, we explain the different instrumentation options we assessed.
Tapping into Existing Instrumentation Some of the basic data we’ve discussed so far—power, cooling, cost, asset, and inventory—can all be complemented by adjacent data. The data you want will depend on the eventual scope of your program. We cover more on program scope in the last section of this chapter but wanted to provide some insight on the types of instrumentation out there. Knowing what instrumentation is today will help you articulate your value in improving it. We need to get back down to the level of Facilities and IT teams to see what instrumentation is currently being used. Finance teams will not have direct insight into how the data they receive is accessed. In almost all cases, it is the Facilities team that has the power and cooling
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| Chapter 2 Benchmarking data you need. In the case of IT, typically some information is available on not just the hardware, but also the utilization of a particular platform (computing, storage, and networking). For example, the data IT would track for efforts toward virtualization will roughly correlate to electrical use. In both cases, IT and Facilities will be the two groups you will work with to give and receive data.
Note Many power and cooling instrumentation options come from the Building Management System (BMS) world. Because legacy BMS reporting systems are not built with global networking in mind, a human hand is often involved in the data that is rolled up to the executive level. Make sure to have an alternative schema to compare data against that has already been interpreted by one or more people. Although you don’t have to be exact at this point, you should be in the ballpark. We’ve seen some power-capacity estimates inflated by as much as 30 percent.
Hard Instrumentation We are referring to hard instrumentation in this book as an instrumentation point that relies on a new piece of hardware to get data. Although it still might communicate via IP, it, in essence, functions like a typical electrical meter. Where possible, you want to get away from these instruments over time to reduce your overall cost. Although they will always have a role and should serve as leverage if already installed, going into the native operating system of an asset using existing Internet protocols is less expensive and less complex in the long run. The best example of a hard instrument is a submeter. Although this is a fairly broad term, it refers to a device that is physically wired into the power distribution of a building. The following are all examples of submeters within the common parlance: Remote Terminal Units Typically communicating via Modbus or E-Mon protocols, remote terminal units connect via RS-485 and report back to a SCADA-type network. These units can be deployed at many points in a building’s power distribution infrastructure. Over the past few years, more and more vendors have been making a web version of these units available for native IP reporting. However, this is far from the norm in terms of deployment. Luckily, the development in this space has recently grown by leaps and bounds. Using a database with multiple protocol roll-ups, such as OSIsoft’s Pi Server, will give you access to these components either directly or through a BMS roll-up. Branch Circuit Monitors These live in the big gray circuit panels we are all familiar with— just like the one in your garage. Branch circuit monitors in most cases are no different from a typical remote terminal unit. Monitoring options in this space are many, but they often get labeled as too expensive. In our experience, expectations for a low price point result from not having any management program to take action on the granular data these systems provide. Finally, when branch circuit monitoring is deployed, it is usually limited to high-density environments such as data centers. Smart Power Distribution Units This is another broad term but typically refers to an inrack PDU that has an IP-network interface (RJ-45). Although a circuit panel is also a PDU, a smart PDU more often refers to the network-manageable in-rack PDUs that live inside of IT racks, also described in Chapter 1. Of all the submetering options, this will provide the greatest level of detail for IT asset power use. These provide current draw per rail and, in many cases, per outlet. Because these power rails report via IP as the norm, no conversion is needed from Modbus protocols.
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Tip If you are lucky enough to have smart PDUs already deployed in your organization, see if you can tap into this data. Although these PDUs are usually found in data centers and labs, the data you get from them might be extensible as part of your analytical model. For example, if you have 10 data centers with a high degree of infrastructure standardization and two of them have smart PDUs installed, you might be able to make some fairly accurate assumptions on electrical usage for the eight that don’t.
Soft Instrumentation Soft instrumentation is where all the fun is, and it’s a big part of what we are proposing in this book. Not many precedents exist here, so you might have few, if any, options available. However, with a little digging, you might get lucky. The best place to start is probably with the IT teams who manage computing (desktop and data center). This is where some of the larger server vendors have been doing some interesting work in the past few years. HP, IBM, Sun Microsystems, and Dell all provide some basic reporting capabilities for many of their server platforms. Furthermore, if you have an IBM PowerExecutive or HP Systems Insight Manager deployed, for example, you might be able to get some detail on specific server deployments. Some of these data sets might be extensible into other asset domains by starting with your server deployments. In addition, some niche vendors have recently come along that are providing shutdown applications for certain infrastructure areas. Probably the best cross-platform solution we’ve evaluated to date comes from 1E. They have a product called NightWatchman, which can be used broadly for desktops but can also handle several types of server platforms. Keep in mind that your approach to energy management is a numbers game—the fewer applications you use to address the broadest range of infrastructure, the lower your costs. If your organization already has something like 1E deployed, you are already ahead of the game.
Note In data center environments, the term shutdown is more or less toxic to both Facilities and IT. Why is this? For Facilities teams, power and cooling availability is their top priority. For IT teams, their biggest priority is IT systems availability. Over the past few years, several new server spin-down features have been inherent to many major platforms but are often left disabled. The main reasons for this are a lack of financial incentive, immaturity of features, and the general lack of visibility inherent to the energy use of IT platforms. This is why we advocate starting with labs and office space, then demonstrating savings through a pilot, and finally graduating to data center environments after you have any bugs worked out. Using this approach makes your energy management program a viable option for data center environments in that it is supported by real, in-house deployments. Although not directly related to power and cooling, data that is being tracked on asset utilization, traffic loads, and time-of-day requirements is something you should keep an eye out for. All of these data points will prove useful later in your analysis, and getting familiar with what you already have is a useful exercise.
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| Chapter 2 Benchmarking Structuring the Data You can approach the structuring of your data in many ways. We provide a basic guide in this section based on what worked for us. There is no right way here—how you structure the data you gather is up to you. You know your organization best, and you are aware of the political realities that exist. Our approach worked at Cisco but might not work exactly the same for you. What we provide in this section are the raw materials you will need to structure a sound benchmarking model. Luckily, the scope of data is relatively small and the asset costs are relatively fixed. To that end, a brief recap of the basic data sets you are after is as follows: Watts You will be applying this rate of electrical consumption to groups of assets, business operations, and real estate life cycles. You can also use it as your base metric for both power and cooling in specialized computer environments (data centers and labs). The most useful variation to basic wattage will be to add hours and cost to it. Watts per hour, including an electrical cost, is most often expressed in kWh and allows for an easy conversion to CO2e. Cost You will be expressing the costs of watts across physical assets, that is, buildings, IT, and Facilities assets. You can then align these asset-based cost metrics to multiple business operations. Time You not only will be showing just the typical 3–5 years trending, but also will be looking at usage patterns of energy throughout any given day. Carbon You can easily include CO2e as a Green metric today. In the near future, you can tie this into your cost metrics through utility rebate programs and the governmental carbon taxation programs that are sure to come. Buildings If this information is accessible, you could opt to align the preceding metrics to a planning or management construct. Buildings (provided that they are owned) are an ideal organizing principle for your various data sets. Many other variations of these basic metrics occur, but this is where it all starts. Making your model more complicated at this point could be self-defeating. Keep the models simple. Prioritizing tasks in terms of achievability will keep the scope narrow and avoid rat holes. As you go through this process, you will learn a lot and will be one of just a few in the world who at present has done it. At this stage, it is important to keep it simple, stick to these base metrics, and keep them mapped to stakeholder interests.
Tip Keeping your data model highly modular is a best practice. This can be as simple as having different worksheets in Microsoft Excel with corollary PowerPoint decks for each sheet. The roll-up of these modules can best be done in a master project Word document. In our case, we simply used the existing templates Cisco IT had for rolling out a new service.
Program Scope Hard-boil your scope so that when it hits the floor, it doesn’t explode. What we mean by this is that you rarely get a broader technological and operational scope than what comes with energy management. It is a mammoth concept with little technological and no operational precedents at the level you are working toward. Simply put, ever since there was an IT department, nobody has turned things off en masse—that is, the “lights are on” 8,760 hours a year. That is the norm,
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and you are challenging it. To mitigate the pitfalls you are sure to find, keep your scope rock solid. Do not let it bleed into areas that are not financially appealing or technologically viable. Why does all this matter when considering how you structure your data? Because how you structure your benchmark data will, in many ways, be telling the story of what you want to do with your program. You will set many scopes on your path to building an energy management program for your organization. The scope we are discussing in this chapter is for a POC. We discuss more scoping models in subsequent chapters, and we recommend you read the book all the way through before starting on your benchmarking data and POC scope. The reason for this is that you will probably need to show where your POC is going in advance of a pilot, program, and service. Try to keep that level of scoping in your vision bucket, and keep your program- and project-level scoping separate. The scope of your POC is phase 1 of your project scope. Examples of the three types of programmatic scoping we employed are shown in Table 2.2.
Table 2.2:
A basic programmatic taxonomy well
Phase
Description
Deliverable
Vision
The focus for the vision phase of your program development includes your strategy, goals, and business benefits. The points you articulate in your vision will set the stage for the scope of your entire program. Dream big, but be careful what you wish for.
At this point, a solid, wellresearched PowerPoint deck should be all you need to socialize your vision to develop energy management as a new service focus within an IT operation.
Benchmarking
In this discovery phase, you are setting the scope of data the program you develop will eventually manage. Although this scope of data will certainly expand over time, it is important to keep things simple when disruptive technology is involved.
Here you will be providing a stake in the ground for your organization’s energy use. The benchmark data sets established here will be the metrics you measure success against. Excel should suffice at this level.
Proof of concept
In this stage, you set the scope of technological and infrastructural considerations. In the benchmarking phase, you will determine what infrastructure is consuming what energy. To implement a proof of concept, you will need to apply targeted technologies to reduce this use. There is typically a blending of the benchmarking and proof-ofconcept phases.
This is a standard proof-of-concept lab implementation. Depending on where you go with the technology, you may not actually need to stage or relocate infrastructure, and you may opt to test on an existing environment that is non-missioncritical. Any existing company templates for POCs can be used in most cases for this deliverable.
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| Chapter 2 Benchmarking Table 2.2:
A basic programmatic taxonomy well (continued)
Phase
Description
Deliverable
Project Pilot
After many of the philosophical questions on technology and finances have been addressed, you can then turn your attention to the operational impact and logistical implications of a new program. Your project plan will include a full pilot scope: resource requirements, time unit program implementation, and organizational dependencies. You should solicit the help of professional project management resources wherever possible.
This is a standard project plan and can be articulated using existing templates. Both Facilities and IT have templates that can be leveraged and in our case, we used both to represent requirements to each group in a format with which they were familiar. The project plan covers the full scope of a pilot if a pilot is required.
Program
At this point, you are aggregating content from prior efforts and putting them into a programmatic framework. You are also ready to move to full implementation and a scale-out focus. Providing your program is scaling well, you start this whole process anew and focus on adjacent resource management domains.
This is the full delivery of the vision you articulated at the beginning of the process. Hopefully, your vision has some room left to expand, but at this point you have established your program.
Service
This refers to an IT service that is delivered to business units at a semi-fixed cost. Email, payroll and web hosting are all examples of IT services.
This refers to a future state this book addresses the strategy to achieve. It involves the full integration of your program into the current suite of IT services for your organization.
How you structure your scope should also accurately reflect your current resources. If you are going it alone, you can get only so much done in a given day. You will be more effective if you can collaborate with other teams. In doing so, you can work with them to build out your scope so that it is achievable. You should also be able to get your peers to pitch in some hours for your effort. How to be realistic in your project management plan in terms of human hours is covered in greater depth in later chapters. Most people at this point are not looking into energy management as a full-time job, so you will need to run lean.
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Tip If you eventually want to manage the program you seek to build, take the time to include full- and part-time human resource requirements in your program scope. You should be able to run a POC with spare cycles from other teams, but to take it to the program level, you will need dedicated resources. After you can demonstrate value through a POC, you are in an ideal position to ask for resources because their cost will be justified.
IT Assets Only If you are an IT person by background, you might already recognize that starting with a program that takes control of the assets you already manage is the path of least resistance. After all, starting with our own backyard is the Greenest thing any of us can do. If you want to go bigger and cover Facilities assets initially, then go bigger—but recognize that this will be a long road just within IT itself. This section and the next describe two possible scoping strategies. Do you start with IT as we did, or do you want to extend the initial scoping of your management program to include Facilities too? When we set out our initial vision, project, and program scopes, we called out IT as the first grouping of assets we would address. The second phase was to be focused on Facilities assets, but for monitoring only. The third phase was to include control of both IT and Facilities assets. It might seem like a logical flow, but you should ask yourself early on whether you want to get into the Facilities assets at all. You will, of course, get savings from cooling thermostatic controls, but you won’t know the amount unless you go down the path to at least monitor them. This, and the fact that we had deep experience in both Facilities and IT, put us on an iterative and expanding path. Whether you want to do more or not, this is where you start. Your benchmark data will reflect the IT infrastructure domains you want to target. This refers back to the bubble chart examples we provided earlier in this chapter. After you’ve prioritized assets by their collective “energy footprint,” you can decide where you want to start. We did it based on our backgrounds and what fields of expertise were available from our colleagues. Networking infrastructure, desktops, IP phones, and some servers were included in our POC scope. With our infrastructure domains selected, we looked at the operational aspects. Our technological approach couldn’t do much more than implement basic time-of-day policies without incurring new integration costs. For that reason, we chose to keep the operational parameters to basic time-of-day usage for the infrastructure. However, it doesn’t stop there. Why is the infrastructure there to begin with? All of this stuff is out there to support business applications, of course. Services and application profiling on some level will be needed in your proof-of-concept scoping. This is where you will eventually need to spend a large portion of your time when scaling out after a successful POC. Play it safe initially, and include only non-mission-critical environments for the first phase of your project. Structure your POC accordingly. The safe bets for initial energy control will typically be found in labs and secure branch facilities. Areas such as this, where you can bring down some, but not all, applications are ideal. For the sake of your POC, start with a lab environment to prove the business case while you identify pilot deployment sites. This enables you to focus solely on the infrastructure you know is represented in volume across the organization. We cover more on this in subsequent chapters, when it is time for deployment.
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| Chapter 2 Benchmarking Note If you try to re-create every environment in your organization for proof-of-concept testing, you will not be able to scale effectively. Focus on the overlay dependencies of applications across infrastructure model types (for example, Catalyst, Sun Fire, ProLiant, and so on). Later, you can map these against buildings and business operations in your program to get a more complete picture of application dependencies. Now you have added infrastructure, operations, and risk to application availability to be considered as part of the scope of the data for your benchmark. If you are focusing on IT assets only, this is a fairly simple analysis. In our case, we used a spreadsheet for tracking purposes and a PowerPoint deck to present our intentions and, upon completion, our findings. Table 2.3 shows an example of the top-level benchmark analysis with some names changed to protect sensitive data.
Table 2.3:
Analytical framework for benchmarking IT assets
Building ID
Infrastructure Category
Billing Period
Total Cost (USD/kWh)
Emissions Equivalencies (tons CO2e) This field will often include CO2, CH4, and NO2 to underpin the conversion of watts into CO2e.
This field enables you to determine an aggregate real estate requirement across the enterprise by inputting building energy data and contrasting it against IT infrastructure requirements.
This field enables you to sort out electrical costs by infrastructure platforms such as computing, networking, storage, and mechanical. Backing these requirements out from building energy use will provide a high-level design efficiency metric for real estate.
This field enables you to trend out energy usage over time and make better predictions of future capacity requirements. This will also enable you to set a benchmark date to work against.
This field gives you both cost and total wattage requirements, which can be converted into CO2e.
Building Example
Infrastructure Example
Billing Period Example
Total Cost Example
Emissions Equivalency Example
Providence, RI
IT Assets —Network
09-Jan–10-Jan
$35,756
8,970
IT and Facilities Assets Adding non-IT assets into your initial benchmarking exercise can significantly increase your value proposition. However, it will also proportionately increase the complexity of not just the
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Structuring the Data 69
benchmarking exercise but the overall program. If you are an IT professional with no background in this and want to include facilities assets, you might consider working with a counterpart in your Facilities operation. This is how we approached the issue. At the time of writing this book, we also did not have access to allow for full control of facilities infrastructure. So we included Facilities teams throughout our scoping but pushed their involvement out to later phases to coincide with new facilities management capabilities we knew were being deployed. As we discussed in the “Collaborate and Conquer” section of Chapter 1, energy management is a big challenge. Bringing in other professionals to fill gaps in your skill set makes sense when dealing with an issue of this magnitude. For the sake of benchmarking, this can go both ways— that is, if you’re from a Facilities background, bring in an IT person, and vice versa. Either way, if you include both Facilities and IT assets in the scope of your benchmark, recognize that you are setting the expectation that the resulting program will address both. The good news is that the base metrics you align to facilities infrastructure can be the exact same as IT. You are aligning watts, cost, time, risk, and organizing principles (buildings, labs, data centers, office space, and so forth) to the assets you hope eventually to control. If your background is in IT and you don’t have a Facilities counterpart, putting together this framework might help you find one. Conversely, it might initiate a defensive response from your Facilities operation, so tread lightly and disclaim heavily. You can do this simply by pointing to the framework and asking for help in developing it further.
Tip We opted to use the same benchmarking framework for both Facilities and IT assets. Using the same approach to both might appear to break down with heating, ventilation, and air conditioning (HVAC), which is often measured in British thermal units (Btu) or tonnage of cooling supply. At this level of benchmarking, British thermal units are more detailed than what you need. You can simply measure HVAC in watts based on a simple burden-factor calculation and convert British thermal units to watts. The proportion is 341.2 Btu to every 100 W. For tonnage, it is roughly 1 ton to every 3,500 W. You can also see an explanation of, and a power calculator for, a cooling burden factor at the Cisco Efficiency Assurance Program site (www.cisco.com/ go/efficiency), under the Planning Tools section. Another indicative data set to employ in aligning facilities to IT infrastructure is a power usage effectiveness (PUE) ratio. This is a basic ratio of building energy allocation contrasted against IT asset energy allocation and developed through The Green Grid (www.thegreengrid. org). This industry consortium is a great resource to join. Content on their site is publicly available. PUE and the associated metrics you find here will prove useful in helping your Facilities operation target and improve on the efficiency of their facilities-support models. You can arrive at the PUE metric by simply backing out the IT electrical requirement and comparing it against total building power in a ratio expressed as follows: power usage effectiveness = total building power ÷ total IT infrastructure power To pull both facilities and IT assets together, you can simply extend the model we discussed earlier in the “IT Assets Only” section. Now you can add to this air conditioning, air handlers, UPS, transformers, and any other facilities assets that are directly related to supporting IT. It definitely helps to align this to a real estate construct and an operative context such as buildings and IT function (lab, data center, office space, and so on), respectively. Table 2.4 provides an example of a benchmark framework focusing on both Facilities and IT assets.
IT Infrastructure Example Computing
Operations Example
Lab 1
This field enables you to break out IT infrastructure domains such as computing, networking, and storage that are specific to a particular operation. For example, you can filter your data to show a highlevel executive how much computing is devoted to labs, data center, and office space across the company.
IT Infrastructure
$187,356
IT Monthly Cost Example
In addition to basic cost trending and analysis, this field enables you to expose what cost is related to what operations and to IT infrastructure. This data will be useful to integrate back into any asset management systems that are already being used.
IT Cost (USD/kWh)
CRAC
$393,448
Facilities Monthly Cost Example
In addition to basic cost trending and analysis, this field enables you to expose what additional, non-IT cost is related to which operations. This data will be useful to integrate back into any asset management systems that are already being used in a Facilities department.
This field enables you to establish an affiliation between groups of IT assets and the missioncritical facilities (UPS, CRAC, PDU, and so on) that support them. This data will be useful in modeling shutdown scenarios and any consolidation that might result from more-efficient use of energy.
Facilities Infrastructure Example
Facilities Cost (USD/ kWh)
Facilities Infrastructure
An analytic framework used to benchmark IT and Facilities assets
This field enables you to establish an organizing principle that aligns to existing billing and management constructs. Typically labs, data centers, and office environments already have some level of asset management to which this field can refer.
Operations
Table 2.4:
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Structuring the Data 71
Benchmark Framework By now you should have a good sense of what data you need, where to get it, and how to get permission to access it. It’s now time to consider how you want to structure and deliver the data back to the stakeholders that will support your program. To that end, please take what we’ve provided here as a framework you can tweak to fit your specific needs. We did this at a large, progressive IT company born and raised in a socially Green place (California), so there was a lot of support for this approach before we ever presented it. Your context might be different, so feel free to chop this up and fight the battles you can win when you can win them. The main points you want to convey with your benchmarking efforts are, in essence, the same as any other business case out there. You want to demonstrate a net/net result for your efforts and in a context that stakeholders will understand. You may not be able to get all the data you would like to have, but defensible assumptions can be made when data is unavailable. Calling out assumptions and documenting them centrally is a good best practice to keep your business case transparent and credible. As you establish a framework for benchmarking, you will be framing the eventual program. Your framework needs to make it clear that part of the value you bring in establishing energy management is improving on the current data. You will be shedding many assumptions as you go through a proof-of-concept and pilot phase, so don’t spend too much time debating the accuracy of existing data related to IT’s energy use. The examples of the frameworks we cover in this section are for benchmarking only. Your technical, project, and program plans will be more extensive and covered in later chapters. To get started, we found that the following attributes were integral to our overall benchmarking framework: Power Capacities How much power is being used, and how has this usage trended over the last 3–5 years? This is IT’s production power, meaning the number of watts an IT asset needs to do its job. It is separate but directly related to cooling wattage requirements. Cooling Capacities This indicates the amount of wattage dedicated to provide cooling to your IT assets. For the sake of simplicity, we recommend keeping this in watts but separating it from IT’s production power. Carbon This might be out of scope, depending on your business context. We chose to include it and used CO2e. These directly correlate to your watts, and they will vary region by region depending on what fuel sources are used to generate electricity. Business Growth For basic capacity analysis, you will need a growth figure of some sort that tracks IT and Facilities assets against overall business growth. IT and Finance departments are usually already tracking this data at some level, and assumptions can usually be made based on what they are tracking. Time Not just per year, but time of day will be important as well. For the sake of benchmarking, we did not include time of day directly and chose to address that in our POC phase. However, if you have access to time-of-day information for IT loads (computing, network traffic, disk allocation, and so forth), you might consider including that as qualitative data up front. Assumptions At this stage, you will need to make some basic assumptions on energy usage across Facilities and IT infrastructure. This is to be expected, and you shouldn’t run into any showstoppers if you call them out as such.
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| Chapter 2 Benchmarking However you end up structuring your benchmarking model, don’t forget that it will be setting the tone for your larger program. With that in mind, keep your framework somewhat flexible and extensible. You can start simply by tracking your benchmark data in a spreadsheet and, as you make changes and improvements, preparing for its database integration. We’ve provided both an assets and operations perspective based on how we approached our benchmarking framework.
Start Small With the basic data sets we lay out here, see what you can gather today without deploying any specialized instrumentation. Because you will probably be working this into your day job, get what data you can to sell your management on your time requirements to establish a benchmark. In our case, it took two people working in concert part time for about seven months to get through a solid benchmarking model at the IT asset level. We kept the benchmarking effort succinct and relevant and positioned it in simple terms as follows: Today we don’t know much about our energy use beyond the utility bill. A benchmark effort is needed to estimate how much we can save. From there, we can determine what technologies will get us there and what will be our net cost to do so.
Assets The assets framework for your benchmark will depend on how aggressive you want to be with your program. For the sake of example, we’ve included a roll-up of both Facilities and IT assets. Again, these represent the facilities that directly support IT and do not include things such as process line controls (PLCs), emergency power offs (EPOs), physical security, and dedicated analog telephony. If your goal is to expand the participation in your program to a virtual team model, including assets outside your domain of expertise can serve as a means to that end. Just be careful to present your program as one that seeks to aggregate efforts, not one to manage these efforts directly in a top-down model. This will help ensure the political viability of your value proposition. Table 2.5 summarizes how we approached the assets for our benchmarking effort. We chose these assets based mostly on the technology we had available to us. In our case, we had already spent 24 months building bridges across Facilities and IT operations and took an aggregate approach, not a top-down. If you have not built these bridges, this can be the driver to do so. Just tread lightly because energy is the most critical service for the business.
Operations Along with your asset benchmarking scope, you will want to include a simple matrix aligned to business operations. We chose to include buildings as part of our operational alignment. Depending on how your finance and real estate teams currently track building and operations, you may need to split this out. Buildings in most cases cater to mixed occupancy, meaning you might have sales, marketing, and tech support all sharing the same floor. In turn, you will most likely have labs and data centers supporting multiple business functions. A good workaround we discuss later is to focus on the application layer as an abstraction of business functions.
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Structuring the Data 73
Table 2.5:
A simple roll-up of the assets for benchmarking
Labs
Energy Allocation
Average Building Profile
Energy Allocation
40%
Storage
21%
Lighting
15%
Computing
38%
Computing
14%
HVAC
35%
Network
19%
Network
58%
IT Infrastructure
40%
Appliance
3%
Appliance
7%
Other
10%
Total
100%
Total
100%
Total
100%
Production Networks
Energy Allocation
Storage
Figure 2.12 shows an alignment matrix of business operations to buildings along with a summary of the largest power-consuming IT assets. This construct is less about the project and technology plan and more about getting consensus on your approach. Operative considerations will need to be addressed with the current domain managers to get your benchmark, POC, pilot, and subsequent program off the ground. The challenge we all face in the conservation business is limited financial resources. We have to prioritize if we’re going to be successful. Jim Howe
Figure 2.12 To articulate the business context of your benchmarking effort, you should take the time to align your measurements to business operations.
Energy Management EnergyEfficiency Methodology
Utility
Cost and CO2
Real Estate
Bill back and Upgrade
IT Efficiency
IT Operations
Bill back and Save
Facilities Efficiency
Mechanical
Thermal Policies
Electrical
Automation
Structural
Floor and Racks
Cabling
Power and Data
Business Operations
Tiered Management Regulatory, Compliance
Capacity Planning
Tiered Alignment
Electrical
Milestones
Mechanical
SLA
Structural
Consolidation
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| Chapter 2 Benchmarking The Bottom Line Convert units of energy into different metrics and across different assets. You will need to have a basic understanding of how energy units are quantified monetarily and then converted. Watts will provide you with electrical capacities over time. With capacity and time, you can simply add in costs in the form of kWh. After kWh is determined, you can easily convert into CO2e. This basic process will apply to both Facilities and IT assets and can be aligned against existing management constructs such as building energy. Master It Calculate the total power requirements in watts based on the data sheet or asset/inventory list power figures for a subset of your infrastructure. Take your wattage and apply a cost to it (kWh) by using your local energy information website. After costs are determined, convert kWh into CO2e by using sites such as the U.S. EPA’s eGRID resource page. For other countries, you may need to check with the local utility provider to determine a CO2e for your local fuel source. Practice and apply the basic principles behind energy efficiency, including electrical losses through distribution, conversion, storage, and use-case workloads. Although you don’t need to be an energy expert to implement energy management, you should work toward being one. Take the time to work through the equations that underpin energy management, and in doing so, you will learn the basic physics of electricity. Master It Commit to a schedule of self-study to include 20–30 hours per quarter. This is roughly 2 hours per week of committed focus. Certainly spend more hours as time allows, but this should be a nondisruptive time commitment. We highly recommend some of the basic course work from the Massachusetts Institute of Technology (MIT) that is available for free on iTunes U. If you want to dive deeper, APC’s Data Center University provides sound data center fundamentals that are broadly applicable. A lot of free courseware is out there, so dig in and learn more. As you get through your first two quarters of self-study (providing you are a novice to begin with), we suggest digging into the publicly available content coming from consortia such as Climate Savers and The Green Grid. The content on these sites will help you to move past basic electrical efficiency and into how energy usage varies based on the type of work (workload) an IT system performs.
Chapter 3
Assessing Value Through your efforts to structure benchmark data sets across infrastructure, real estate, and operations, you will move on to prioritizing your deliverables. To ensure that your program can be established and scaled, you will need to assess the value of the many potential deliverables involved in energy management. This chapter provides a basic methodology that can be used as a guide to establish value specific to your infrastructure and operations. Consider the framework laid out in this chapter as a guide that focuses on some common value themes across facilities and IT departments. However, in all cases you will need to tweak this framework to the specific interests of your stakeholder groups. In this chapter, you will learn the following: •u How to best structure the data to support your program’s goals and deliverables •u How to convert and normalize the data across different operations and disparate infra-
structure •u How to best present the data to highlight the multiple levels of value your program will
bring to the existing organization
Organizing the Data Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted. Albert Einstein Think of structuring the data that supports your program just as you would build a house. You build it from the foundation up, and you make sure the foundation is strong enough to support the weight of the structure. There will be many permutations of the foundational data you maintain, but they will come later. Because you are building your benchmark, proof of concept, pilot, and program structure virtually at this point, you will have to make some assumptions. However, try to limit assumptions where possible and avoid any “deep dives” on the data that rely too much on theory and not enough on real data. When building a home, you don’t need to do a structural load test on concrete, but you do need to use a material (concrete) that is a proven stratum for construction. Power capacities, cooling burden factors, kilowatt-per-hour cost, and carbon dioxide equivalencies are your concrete. Buildings, square footage, departments, and system integration are your sides, roof, and trim. There are several vehicles to consider when organizing your data. We opted to keep it simple initially and used basic spreadsheets with pivot tables and, in preparation for the pilot setup, used a Structured Query Language (SQL) database to cube the data sets for modeling purposes.
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Generally speaking, we found that the best vehicles to assess and express the value of the data you’ve mined include the following: Project Master Document We used Microsoft Word and leveraged existing templates from within our IT operations. The project master document will be the most detailed and extensive of all your content vehicles. Typically, companies have a tracking system already established for these types of documents. This document is also used as part of a formal proposal to IT and facilities teams. Excel Spreadsheets Good old spreadsheets work very well because initially your scope of data is fairly small. You are primarily looking at watts, cost, CO2e, and time. We also broke our scope down into IT assets, IP addresses, location, grouping (computing, network, storage, and other), and departmental ownership. PowerPoint Presentations Having a good set of presentations to convey your intent is critical. We structured one deck into three modules: Executive Communications This deck hosts your vision, strategy, and execution planning. You should try to keep this deck to 15 slides or fewer. We show some examples later in this chapter that display some of the data points. Director Level This is where you cover your specific resource requirements for the effort. Because you will need to build a virtual team, this deck can also serve as tracking for your virtual team members’ contributions. Team Planning Because there is no precedent for this level of energy management, you will need to coordinate individuals with different backgrounds. This deck will serve as your functional project plan and will help you keep everyone on the same page These content vehicles will serve you well, but will not cover the operational elements of a new service. Said another way, these tools will get you started but will not scale to support a new IT service. There will be a definite need to store this data to show the long-term value of your program. We cover more on operations, storage, and reporting in later chapters.
Finding a Database There are many ways to approach the database support needed for your program. In our case, we assessed three database options. We looked at SAS, OSIsoft, and SQL. We found that OSIsoft best fit our needs from an operations standpoint and used SQL for the up-front financial data modeling. What we did not do was establish a new database for the back-end operations. We intentionally looked to integrate energy management data sets into existing database constructs. There are numerous databases you can choose from that will suit your needs for tracking and storage related to operations. In Chapter 5, “Building a Pilot Deployment,” we discuss the necessity of setting up an independent database for your pilot phase. We approached the facilities, IT, and finance teams, and we found that all tracked some piece of energy use that was relevant. The facilities and IT teams had their own functional tools for tracking, but energy costs were also tracked by the finance teams. Mining the data that you need for your program through your finance department is ideal. Integrating your data processing, translation, and storage into the databases already in use by the finance department should be the first option you consider. Logistically speaking, this makes the effort required to gather energy data easier for the IT and facilities teams. Finally, it can save you the overhead of a new production database to establish and maintain.
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Organizing the Data 77
You might find that your finance team will push back if you are asking for energy data on behalf of any other business function. If this is the case, you will need to sell both facilities and IT teams on the idea of supporting the project first. After you have their executives’ support, they can either sponsor your request into the finance team or directly support your database requirements. If you must build a new database, then so be it. In our case, however, we didn’t have the time to commit to a new production database. In general, our strategy was to prove the model and integrate energy management into what was already in place. Getting energy implemented as a new service within IT is difficult enough. Advocating for a new fiefdom with new database overhead on top of it just adds to the challenge.
Tip Building your data into the systems that are already in use will help your program scale better globally. Given the youth of energy management technologies, the professional talent base is small, and you will more than likely need to scale virtually. Although there is some “selling” involved to get supporting resources from other teams, it is a reality for which you must prepare.
Ensuring Data Quality The Russian proverb doveryai, no proveryai translates to trust, but verify, and it definitely applies here. What you will find in looking into electrical provisioning, monitoring, and billing is that there is a lot of estimation. Electricity is rarely an exact science in terms of its application. As a global economy, we have over provisioned power requirements and estimated cost allocations for years as a practice. This is to be expected when you have completely non-networked instrumentation or none at all. Building management systems and IT networks don’t have a common standard from which you can monetize and allocate energy today. With all this as background, the data you weed through in the early stages of your program will be prone to compounding inaccuracies. These primarily come from the wide range of reporting sources and the many efforts to translate between requirements.
Note Data on electrical requirements is often overestimated on one hand or averaged out on the other. A good example of this is data centers in which the size of the power-support systems is often larger than it needs to be (for example, a 480 kW UPS is specified for an initial load of only 100kW). Unless a data center is set to monitor energy use down to the IT assets, the business often gets billed for the total UPS loading. If this UPS has been over provisioned, the business is already paying for more power than it needs and dealing with the inefficiencies that come with it. There is a fine line between ensuring that your data is credible and proving its overall quality. The program you are building is an effort to improve the quality of the data the business currently gathers related to energy use. It’s important to communicate that up front, because it can be difficult to defend the fact that most benchmark data you will get is built on incomplete instrumentation. However, you should be able to get support in targeting a future state of energy monitoring where there is no more than a 3–5 percent variance in your data in the early stages of your program. This will improve over time as the instrumentation and normalization technology improves. After all, this is the first generation of IP-based energy management, and it is intended to track an order-of-magnitude of savings. In this regard you don’t need to sweat the small stuff in the early stages of your program’s development.
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The following are some basic steps that you can take to ensure that you are building a credible foundation of data: Facilities Energy Information Information on facilities’ energy use is often taken from a manual input coming from a building meter or the kW size of a UPS or CRAC unit. In many cases, the actual draw varies widely from this input. For UPS and CRAC units, first check whether some type of central monitoring is deployed already, such as Liebert SiteScan Web or APC ISX Central. These systems will give a close-to-real-time roll-up for large power and cooling systems that can act as a proxy for the IT infrastructure they support. If you don’t have this, it is usually safe to start with the nameplate rating of the UPS or CRAC and then take 30 percent off the top of the input rating as an average. This is less than ideal, but far less costly than taking local readings across hundreds of sites.
Tip Because you will be bringing on facilities assets in later stages of your program, the up-front estimates can tolerate some variance. As part of the discovery you will go through with both facilities and IT assets, you will get close-to-exact figures. Too much time spent here trying to be perfectly exact will ultimately be redundant with your actual data discovery phase. The instrumentation you deploy should cover this. IT Asset Information You will likely have no centralized source of information for IT asset energy use that is credible today. At best, you will have an asset inventory list with a column for energy use that is often input manually during product registration. If this field is optional in the registration process, you will probably have major gaps in IT energy data. You will also need to contend with the many IT assets that use no power at all, such as racks and cable trays. In our case, we manually scrubbed the repository with the most assets, and we input a nameplate power rating and normalized it by removing the 30 percent off the top.
Tip Just as with facilities assets, you will need to make assumptions about IT energy usage. The same normalizations can be used, and estimations can be made of the savings. Again, you will be receiving live data after you’ve deployed a pilot and again throughout production. Don’t sweat the small stuff with asset and inventory or you will lose valuable time for little return. Financial Data As mentioned in earlier chapters, many elements of energy information are already tracked and normalized. Your benchmarking efforts will carry well through production, but it is important to know what assumptions IT is making on its current billing constructs. After you understand these assumptions, you can tailor your value proposition back to the finance teams in terms of improving data quality.
Tip Many of the billing systems that have been created for your organization were built at a time when little or no energy instrumentation was available. This is emblematic of many aspects of energy use. It has been cheap, reliable, and unconstrained. Therefore, your financial data on energy use will be vastly improved through your implementation and will be of value to your finance teams. Your initial business case and benchmarking can be only as good as the data the company currently tracks and its interpretation through your own expertise. There are a lot of commonsense rules you can follow if you have a basic understanding of mechanical, electrical, and information technology engineering. If you have major gaps in any of these three areas, fill them
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Organizing the Data 79
in, and you will ultimately improve the quality of data going into your program’s inception. However, the reality is that you only need to be able to credibly demonstrate a respectable savings to get a program initiated. If you can prove and hopefully exceed your projections through a pilot, you will be one step closer to moving towards your program’s establishment.
Note The end goal in assessing and presenting the value of your program is to get it adopted as a service that IT and facilities groups manage. This insertion strategy stands a good chance of succeeding if you show its value within the current context. Because the current context is in a constant state of flux, you might have to compromise on what data you can and cannot access.
Prioritizing Data Your top priority should be costs, first and foremost. Watts and carbon can be fairly accurately inferred from there. However, there is so much data on this small scope of data sets that you should spend some time thinking about what you will prioritize and for whom. We spent a lot of time going through this in our deployment. In order to demonstrate the value of our program, we built several data models for our stakeholders. Each model highlighted different interests but leveraged the same base data at some level.
Finance and Executive The first model you might consider is at the executive and finance department levels. This is where concerns about cost, capacity, and asset management, and brand concerns related to carbon and the environment are considered. This will be the highest-level roll-up you provide, and it is where you can highlight the less tangible, strategic value of your efforts. Prioritizing and quantifying strategic value is a fluid exercise that varies regionally. What you can safely focus on as a priority for the broader corporate strategy will specifically be cost, power capacity, real estate environmental standing, and GHG emissions. Your program will have implications for all of these today. As for real estate standing, you might not directly address an existing certification such as Leadership in Energy and Environmental Design (LEED), but your program is leading. Certifications such as LEED will certainly consider incorporating new approaches as more people adopt them. Executive priorities can be expressed and tracked using a simple data model providing the following elements: Total Electrical This number is a full roll-up from all the points of instrumentation you deploy or consume from other systems. The total electrical capacity of the enterprise is expressed in watts and should track linearly with company productivity in general. However, as you trim the time-of-day wasted wattage, you will see the lines between productivity and energy consumption get closer to one another. Said another way, you will see an improvement in overall enterprise energy efficiency. Total IT Electrical This represents the total wattage being consumed by IT assets. This does not include the power to cool IT assets in specialized or office space environments. In the early stages of your program, this will be an estimate only. Total HVAC Also expressed in watts, this is your first breakout of total electrical consumption. In almost every case, this will be the largest subset of total electrical usage. This
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includes all data center, lab, and office space cooling, heating, pump packages, and water supply electrical use. Total CRAC Relying on a cooling burden factor calculation, this relates to the total wattage required across all specialized (data centers and labs) environments. This specifically measures the electricity required to remove the heat being generated by IT assets. Total Costs Expressed in cost per kilowatt hours, this is the most important data point in terms of your program’s success. With costs understood, you will be able to determine the best points of value your program can deliver. Total CO2e For many companies in the world, this is a “nice-to-have” as of 2010. Unless there are mandated requirements to report or cost implications related to GHG emissions, this is a qualitative data point. However, if you (like us) have a passion for Green, make sure to include this information because it will be important for brand implications, recruitment, and employee satisfaction today. Total Buildings This is simple count of the total number of buildings your company owns. If you can include leased facilities as well, you will paint a more complete picture. This number is useful to track because it will explain any large increases or decreases in total power outside of infrastructure upgrades or energy management reductions. Total Floor Area This is not a necessary attribute in your value assessment, but aligning the organization’s energy use to watts per square foot or meter enables you to show efficiency gains in a real estate context later. This will be useful in later permutations of your program.
IT Operations The second model that helps you prioritize the data you gather will support your IT operations. Costs can be referenced here, but be careful to recognize that this may not even be within your IT department’s top 10 concerns. Most IT operations do not see cost related to electrical supply because it is either passed through to the business or estimated by the facilities department and billed back. Although you can challenge the current system later, for the early stages of your effort, place cost lower on the list. The primary concerns of your IT operation will be related to power and cooling capacity in data centers, risk related to electrical supply, and Green branding. IT’s priorities are many, but cost is not at the top of the list in most cases because of the current cost-accounting systems. IT’s priorities can be expressed and tracked using a simple data model providing the following elements: Total Electrical Capacity This is the total available wattage available to the IT operation today. This figure can typically be inferred from a roll-up of the UPS systems deployed to support specialized environments. Available Electrical Capacity This represents the difference between total electrical capacity and the total wattage currently being used by an IT operation. This often is estimated without actual instrumentation. Your deployment will provide a higher degree of detail on available capacity than the current systems. Total CRAC Capacity This number may or may not be in watts under the current tracking system. We chose to convert total available computer room air-conditioning into watts for
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Organizing the Data 81
sake of simplicity. This can be inferred by rolling up all the CRAC systems that are dedicated to IT operations. Available CRAC Capacity Just as with electrical capacity, this is the difference for cooling. Available cooling is also rarely instrumented (via IP) and, through the simple addition of temperature-sensor networks to your program at a later date, can improve the current system. Total IT Asset Count Often found through asset and inventory lists in the early stages of your program, this is the total number of IT assets currently deployed. We chose to group ours by technical function (computing, network, or storage) and further align them with space and department. Total Facilities Asset Count For the sake of aligning availability and efficiency, you will eventually want to associate IT assets with the Facilities assets that specifically support them. These are typically UPS, CRAC, and genset assets. Total Data Centers This is a subset of the total buildings you might track for other areas of your program. Copious amounts of data will be available on data centers, given their strategic nature. If information on data center energy use is already available, leverage it as best you can. Total Labs Just as with the data center information you might uncover, lab energy tracking can be valuable. If any labs are already tracking their own use, those lab teams could be good to work with and learn from early on. IT Asset Utilization This is measured as a percentage of an IT asset’s total workload capacity. It is often expressed as Central Processing Unit (CPU) for servers, ports for network, and Logical Units (LUNs) for storage. This is starting to be tracked by many organizations as part of virtualization efforts and requires operating system access for each domain. If you can obtain this data, it will make for a compelling point against the data you track for energy use. Total Costs There are many ways you can express the cost to IT related to energy use. If you want to be exact, you can net out the difference between what is billed (kWh) and what is measured. You can work toward this by initially tracking against existing data that you gradually replace as new assets come online. Another useful linkage you can make associated with IT operations relates to aligning your data to other data sets. In our case, there had been a large, ongoing initiative to virtualize storage and computing across Cisco. The Network Data Center Services (NDCS) teams were already tracking our migration from physical to virtual resources. We approached this team to build in data from our program to track along with other efficiency efforts. This provided compounded value to the existing virtualization effort as we could show the power savings corollary.
Tip You can usually find out what IT’s priorities are simply by looking into a company’s global plan and strategy. From there you can cherry-pick your insertion points. If no plan exists, you can simply show your data as improving the operation’s ability to forecast and deliver power. Risks related to electrical outages and unforeseen capacity shortfalls are a major priority for most IT operations today.
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Facilities Operations The last specific data model is intended for facilities and real estate operations. In this model, cost and risk are king. There is typically a much tighter linkage between finance and facilities teams on this data than with IT. Facilities teams are under constant pressure to keep costs down and are highly risk averse. This combination makes your proposition to control energy use an attractive but risky proposition. This is where more qualitative selling than data modeling is needed. You must have the credibility and, therefore, the respect of these teams if you want your program to scale past IT infrastructure. In the first operative phase of your program, you are not directly controlling any facilities infrastructure. However, in this first phase, you bring the facilities team in to learn from what you’ve done on the IT side. This helps you build relationships with them and demonstrate real value. In our case, we simply offered the facilities team a data feed of what was happening and where. Because they would already be getting savings through basic thermostatic controls, this feed gave them a way to quantify some of those savings. This also introduced them to the whole concept of dynamic energy management. Because you are not controlling facilities infrastructure directly in the first half of your program, the data model is purely informative for a facilities operation. The model includes the following data: Total IT Load This initially will be taken from the data already being tracked by facilities and finance operations. However, you can begin to replace any estimates or legacy instrumentation with data reported directly from IT assets. Total IT Costs This can represent a simple plug-in of existing costs under the current accounting system that are billed back to IT or passed through to a business unit. You can later show a change over time to the current cost allocations and what your program will measure. Total Buildings with Data Center Space This field is already well understood by facilities teams, but including it in your methodology will help you model subsets of building data at a later stage. Knowing what buildings house data centers is also valuable information when considering risk in your early deployments. Total Buildings with Lab Space If your organization has a large lab operation, this should be the first area for which you can demonstrate significant savings back to the facilities teams. Facilities teams are already typically tracking building space in most cases. Total Floor Area You can apply a wattage-per-floor-area metric to each operational area of the business (labs, data centers, and office space). Showing improvements in this real estate context will impact both operational costs and new site planning. Total UPS In the early stages of your program, this figure will most likely have the same data source as the Total IT Load. Total UPS wattage can serve as a best-option proxy for IT energy use today. The data you can provide directly from the IT load will be a point of normalization in which the facilities teams will be interested. Total CRAC The same methodology applies to CRAC as for UPS. However, for cooling, not all IT assets report their ambient operating temperature. Two ways to consider improving cooling data are by (1) bringing an IP address to the CRAC units and (2) deploying a temperature-sensor network.
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Translating Data Models 83
Total CO2e Although, generally speaking, there might not be a direct cost allocation to GHG emissions, any pressure to report on this lands on the facilities teams. The teams will be happy to hear you can help them with this.
Note Don’t forget that the facilities and real estate teams have incentive to mitigate risk. IPbased energy management is sure to raise concerns with these teams, and rightly so. Make sure to point out a phased approach starting with IT as a proof point and address facilities infrastructure at a later date. If you posture that IT will soon control HVAC, lighting, and emergency services, your program will be dead on arrival with these teams.
Translating Data Models You will need to worry about protocol conversions later in your program when and if you bring facilities assets online. However, for now you don’t need to worry about deploying protocol conversion. What you will need to focus on is translating data models among each other. This will enable you to model data dynamically between the IT, facilities, and finance departments. This gray area is your niche and where you can take the gray out of Green.
Formulaic Approaches The most obvious data conversion here is from watts to cost. You probably won’t need to do the actual translation as much as find the team in the finance department that already tracks this data. However, the ability to estimate a theoretical cost will come in handy. This estimate will apply to both IT and facilities assets, and you can use a basic formula to estimate the cost of capacity as expressed in watts. For example, consider the operating cost of a 1,000 kW computing system (roughly 1,250 servers using 0.8 kW each) with 60 percent efficiency at $0.10 per kWh (U.S. average for power) for 1 year at a 50 percent load level: Operating cost = 1,000 kW × (1/0.60 efficiency at load level) × 8,760 hours × $0.10 × load level 50% Cost estimate = The computing system uses 833 kW per hour at an annual cost of $729,708. The next conversion you will use consistently is from watts to CO2. Although CO2 is primarily a branding concern, governmental regulations could result in real financial implications in the near future. The United Kingdom and Australia are both implementing systems as of 2010 that could be considered a form of carbon taxation. Outside these countries, carbon management is more about keeping up with regulatory debates and inferring a future state cost than the straight data conversions you can do on an eGRID type of website. There is no formula for inferring a Green cost, but we do provide some thinking on it in the next section. Table 3.1 shows various conversion rates for key regions. If we stay with the example of the computing system using 833 kWh and extrapolate that out over a year, the result is 7,297,080 kWh in one year. If we assume all these servers are U.S. based and you took a measurement in 2007, you would have a total loading of 4,079,068 CO2e per year for that system. You simply multiply your total kWh by the emissions factor for the United States in 2007, as shown in the Table 3.1.
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Table 3.1:
Global CO2 Emissions Factors from watts
Country
2005 CO2
2006 CO2
2007 CO2
United States
0.5754
0.5729
0.5590
European Union
0.3623
0.3409
0.3540
Africa
0.6508
0.6427
0.6450
Latin America
0.2029
0.1970
0.1940
Middle East
0.6966
0.6901
0.6700
Non-OECD Europe
0.4893
0.4786
0.4990
One small complication of this approach is that there is no standard for emissions reporting for fuel sources. Even in the case of coal, there are different emissions factors for different types of coal. When possible, don’t sweat the small stuff and stick to defensible estimates. Saving dollars will also save emissions, and although not perfect, it is an improvement over the current state.
Qualitative Approaches You can show only so much in terms of cost savings. Because you are working functionally with three groups (facilities, IT, and finance), you can’t assume that cost is the number one consideration for each. As mentioned in the preceding section, the IT department is unlikely to be motivated by cost alone, and the facilities department will heavily temper cost-savings opportunities against new risk adoption. Although cost and capacities are the main course, there are a number of side dishes.
Going Green If you follow the current debate in the United States, you will see a massive variance among political economists. The right will tell you that cap and trade will break the bank by causing close to a 30 percent increase in electrical costs over the next 10 years, and the left will tell you it will rise less than 10 percent over the same period. We chose to adopt a play it safe stance and split the difference if partisan politics ever came into play. The better approach is to educate yourself on the foundational economics behind energy usage (GDP, energy intensity, electrical generation capacities, and popular opinion) and deduce your own estimates. We opted for an 11 percent increase in cost over a 10-year period if the business made no changes. With energy management implemented and a more concerted focus to choose new sites with low-to-moderate carbon intensity, we projected an increase of just shy of 4 percent net. This would be a 3 percent reduction to the normal inflationary estimate of 7 percent over 10 years. In our case, our core team all had personal Green interests, and we simply chose to lead by example. We did this by exposing the data, following where it led us, and applying proven technologies and methodologies to clean up our own backyard. After all, Green is a personal choice, and so it is for corporations by extension. If you need to show your corporation the value of going Green, then find the data and expose it in a business-relevant context.
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Tip Play it safe when inferring changes to electrical costs that may result from political action. Although there has been much discussion on governmental carbon management, to date there has been little wholesale action. If new systems such as cap and trade are introduced, it is fair to assume they will not be overly disruptive in the near future. It is safer to adopt a middle-ground stance on partisan estimates and rely on basic inflation to support your case.
Addressing Risk We also recognized that what value we uncovered would need to be contrasted against risk. Make no mistake—there is a large risk in doing almost anything related to power and cooling. They have the potential to kill, literally and figuratively. However, the instrumentation you are providing through your program has the potential to reduce the mid- to long-term risk of both facilities and IT operations. Why? This is simply because, as a practice in IT, we have mitigated risk well through design but not always through operations. This is why there is a difference between availability and resilience of an operation. The latter refers to design and operations, while the former is for “designed availability” only. Like it sounds, designed availability refers only to the planning and design aspects. Your program will face a choice early on: How much time do you have to spend on addressing risk in both design and operations? The technology you choose makes a big difference here. If you opt for energy management technologies that are domain specific, you simply have more designs and more subsets of operations to deal with. In other words, if you commit to one vendor for desktop energy management, another for servers, and yet another for storage, you will have three times the complexity than if you work with a single vendor that can address multiple domains. This was one of the drivers for us to develop Cisco EnergyWise: to provide a cross-domain solution that can address risk across proprietary barriers. In terms of design, you will also want to look for a solution that is backward compatible, to limit new design work. Backward compatibility is another attribute we looked to address our service’s creation. Another attribute of risk that you will want to consider is the larger business impact analysis. Although this will be addressed by your pilot to a large extent, you will want to provide recommendations to IT on the potential impact of your solution. If you don’t have experience with business risk analysis, seek out someone who does. Many of your recommendations will be qualitative, but they should be considered nonetheless. Figure 3.1 provides an example of some qualitative recommendations you can make.
Figure 3.1 Although you might not be able to provide a full business impact analysis, you can provide qualitative points that all add value.
Risk and Reward Operations
Management
Disruption to the current system
More points of management increase human error
Reduced energy costs
Better troubleshooting
Security
Change
Energy management Compromises system gets hacked building management system integrity Recruit and retain employees Improved capacity planning
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In closing, risk is something to take seriously when approaching energy management. You will find your facilities teams have an incentive to be risk averse, and rightly so. Make sure that you can address their concerns ahead of time. This can be accomplished by bringing them into the fold in the first production phase of your program, when you are approaching IT asset energy use only.
Tip Giving your facilities teams the option to be involved early in the project gives them an opportunity to learn your system with no direct increase to their risk profile. Involve these teams early and often because, without their support, you can’t scale your program past IT assets.
Presenting the Data As mentioned in earlier chapters, you will have several levels of presentation throughout your program’s development. In the preceding chapter, we showed how to prepare and present your benchmark data. In this section, we discuss how you can present the value of the different energy management options. This section does not cover technology assessment, which is covered in Chapter 5. What it does provide is a framework of decision-making criteria that can be applied to a variety of technologies. This section is organized into the business functions that you will insert your value assessments into: finance, IT, and Facilities operations. Within these business functions, the base metrics are more or less the same, but you will need to articulate their values in different ways. Understanding each function’s context and hierarchy of value will enable you to assess what features should be included in your program. For example, you don’t want to spend time assessing a facilities technology for an IT staff if the IT executives have no interest in managing facilities infrastructure. Finally, we do not expect every reader to arrive at the same conclusions that we did in our project. Nonetheless, we do think sharing our methodology can help you avoid easy mistakes.
The Context The facilities, IT, and finance departments will all be served well by your core data sets: watts, cost, and carbon. However, each department will have different priorities and responsibilities in acquiring and managing this data. In developing your program, it is important to understand the value each group will place on this data and in what context they will see it as relevant. There are, of course, thousands of possible permutations of context and operation related to the data you gather. We chose to keep it simple and focus on the most relevant and adjacent areas to our key focus area—IT assets. We then tempered the proximity and relevance against the popularity and funding of existing programs.
Tip How deep you take your value assessment is a daily question. We found that cycles spent going too deep into any one area took time away from another. Applying an overlay mentality to frame the operative contexts you are considering will help you prioritize your time when assessing your program’s value. The executive-level context is simply improved energy management. To IT, the context is mainly tied into capacity management and, with some cost-allocation tweaking with the facilities and finance departments, to cost reduction. IT is also thirsty for data in most cases, so inserting energy data as an attribute of other programs is a good subcontext to consider. To the facilities department, the context is primarily risk-centered and secondarily focused on cost reduction.
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Virtualization and Consolidation Hitch your wagon to a star, as Emerson said, can be a great guideline when assessing the value of your program. There are few IT operations that are not seriously engaged in some level of computing and storage virtualization today. As of this writing, most of our customers are already reaping the benefits of server consolidation coming from solutions such as VMware and Xen. Virtualization is typically a top-level initiative for an IT operation. This level of a high-profile, funded project can be an ideal insertion point where you can add value. Your value comes in the form of an additional savings (energy) that may not be within the scope of what is being tracked for a virtualization project. Later in this section, we also discuss a secondary point of value related to the need for power and cooling data when virtual machine mobility is adopted. There is obvious value derived from consolidating older IT platforms by using technologies such as virtualization. For most IT operations, the usual benefits—consolidation, lower operating expense, and the ability to defer new unit purchases—are enough to get a project funded. In almost all cases, however, one of the largest savings derived from a virtualization project is not tracked at all—electrical savings for both production power and cooling is rarely measured and aligned for a project like this. There is often little or no networked instrumentation to do so, and because IT is not primarily concerned with power costs, it is left out of the equation. This is an ideal, low-risk insertion point for your program. You simply put a “cherry on top” of the current virtualization effort by instrumenting and tracking energy savings derived from the effort. You can also point out the value of having energy data related to server and computing virtualization available in the second operative phase (facilities infrastructure) of your program. When you think about virtualization moving to cloud-based services, one thing is obvious: There will be a high degree of VM mobility. Moving computing within a single data center or across the globe would seem to be the norm in the near future. If this is the case, one can assume that large electrical loads will follow these Vblocks. How can an operator be sure that a new landing zone has the power and cooling capacity to accommodate a new electrical load derived from these Vblocks? Without some level of facilities integration to check this, it would be like jumping into a river without first checking the depth and looking for rocks. Conversely, with no ability to spin down or shut down servers, you will be losing an efficiency opportunity on the zone from which you just moved the Vblock. This is exacerbated by the fact that most legacy server platforms consume 50 percent of their top operative power at standby. As you can see, energy information becomes only more relevant as IT moves into the cloud. The last area of value we focused on was to help identify new consolidation opportunities. As you begin to instrument assets and report back on their energy use, certain patterns begin to emerge. You will first notice that productive assets will have a very predictable 8 a.m. to 6 p.m. power curve that correlates to the work they are performing. Then you will notice that production assets that are not currently productive have their own curve—except it’s a flat line. Assets that are reporting flat-line energy use are the ticks on your dog’s back. They are likely sucking power but doing no productive work. After you identify these and make sure to monitor them for a full four months, you can then call them out as candidates for decommissioning or redeployment. An internal standard is a good tool to employ later on and call these assets out as noncompliant.
Tip Check across the IT operation to see what dashboards are tracking what resource data. You might find that the teams are already tracking data such as asset utilization, VMs, hardware platform asset count, and so on. Each of these dashboards might derive value from having energy, cost, and carbon data embedded. Meet with the respective teams to assess the value they perceive in including data from your program.
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Qualitative Business Benefits The most obvious benefit today is mainly qualitative, but in time it can be made quantitative. Green in the form of GHG reductions has a clear brand implication but also has a “bragging rights” draw. Many corporations, governments, and non-governmental organizations (NGOs) are working hard toward an actionable, high-impact solution to reduce their emissions. Although some opt to do this with money through carbon offsets, the popular understanding is that this will not bring about a cultural change. Shutting things off when not in use is a change to the always-on culture we arrived at today. As Dean Nelson at eBay likes to say, Go from always on to always available. We had some experience with efficiency initiatives and drove large projects that did result in some real gains. However, most gains were in the single digits at the corporate level. Before now, there had been no broadly extensible foundation to build on. Not until we established EnergyWise within Cisco networks could we start effecting double-digit reductions at the corporate level. Our expectation in writing this book was not only to provide qualitative detail, but to provide a path forward to quantifying and incrementing your Green efforts. By default, you will need to speak to the Green value of your program qualitatively in the early stages. However, on day one of your program’s inception, you can start accurately quantifying what had to be abstracted in the past. Granted that this will be only your IT infrastructure, but for many large corporations today, as much as 50 percent of the total power goes to IT systems. Whatever gains you can make across IT infrastructure will nearly be matched by facilities savings through basic thermostatic controls. The next evolution of your program can clean up the remaining facilities’ corollary savings through intelligent building controls. You can certainly provide quantitative data that is not exact but credible when leading up to your program launch. As long as you can get a sound roll-up of energy use in whatever context, you can convert it to cost and carbon. For many teams, this will be the first time they see this information within a given context. This is a good point for feedback they can provide to help you determine the value you can insert. There will likely be less-obvious benefits you uncover as you go through these processes. One area we saw that paralleled our effort to provide unified communications technology as a company related to Cisco TelePresence. This technology was developed as a way to meet faceto-face with more customers, partners, and coworkers while spending less time traveling. We happily took advantage of this technology in our program. However, you will not necessarily reduce your emissions related to air travel by just acquiring the technology. The technology needs to be deployed in conjunction with corporate travel management. Otherwise, you run the risk of traveling just as much while increasing energy use for video collaboration services. As long as air travel is cheap, there will be a preference for face-to-face interaction for certain meetings. However, the reality is that travel time is generally not productive time, and if you can shed even 20 percent of it with a fresh alternative, that’s great. We learned a lot from the very positive cultural reception of TelePresence at Cisco and incorporated it into our program development. The qualitative lesson we learned was that you can mitigate resistance to change around energy reductions if you provide a decent alternative. Greener, not grayer, integrated, monitored, and measured are all qualitative attributes for better managing your energy. How you implement these attributes will vary, but they serve as guiding principles of your energy management program. Understanding the context and the difference between what you can quantify versus what you can only talk about keeps your value assessment viable.
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Comparative Models With an overlay perspective in mind, it will become clear you need some level of mapping and matrix work. With the finance, IT, and facilities departments, you should be working toward developing a high-level management dashboard that each group can access to interpret what you report. More on dashboard creation is covered in later chapters, but the analytic models you build will largely shape the structure of any dashboards you build later. The first model to consider is for facilities operations. This is where you will be able to aggregate most of the baseline data and present it in close to raw form. In the pilot and proof-of-concept stages of your program, you can likely use the same model for finance and facilities. Your facilities model should include the following attributes at a minimum: Total Capacities We chose to express both power and cooling in terms of watts. We decided that we didn’t need to go too deep on the cooling front and left that to the facilities teams. In subsequent operative phases of our program, we scoped in the activity of associating IT assets with cooling units. However, in the first phase, we determined this was out of scope. Time-of-Day Costs In our case, we didn’t need to worry about this. However, in all cases, check with your finance and facilities teams to see whether your real estate is subject to timeof-day billing. This can cause a significant variation in your financial modeling. Billing Period This will enable you to see an annual bell curve for enterprise energy use. This curve will primarily follow your organization’s productivity and the weather. As you instrument, you will be taking this from annual down to hourly. Building Readings Of course, you want to align energy use to buildings for logical management purposes. What you might also find as you mine this data is that different building departments are typically billed individually. You will eventually want to show specific business-unit savings as well. Make sure to check with your finance teams to see whether this is the case. Business-Unit Billing In some cases, finance teams are directly billing the company’s departments for real estate costs. Power and cooling are often rolled into this cost along with depreciation, taxes, insurance, and so on. If you can get access to this data for your model, it could help you in structuring your energy domain management architecture later. Floor Space This field is usually associated with planning and billing for electrical and mechanical systems. However, including this will help you filter your data later in data center environments to show an efficiency gain that is universally understood.
Tip There are many different approaches to real estate billing and even more for power and cooling allocations. Because your base metrics are foundational elements of all of them, you will be educating yourself on your company’s systems as you go through the process of gathering data. If you take an insertion strategy with your program, the process of accessing the data will ultimately help shape it. In other words, you will uncover the people and process elements of the existing billing systems as you engage the teams that manage them. To pull this all together, you can start with basic spreadsheets. After you set up a master sheet and pivot tables for the data, you can assess what database to use. In our case, we used SQL, as mentioned earlier. Table 3.2 provides an example of what attributes we used to model facilities interests.
100% 71% 61% 100% 35% 70% 53% 87% 71% 59% 56% 100% 78%
Lab 1
Lab 2
Lab 3
Lab 4
Lab 5
Lab 6
Lab 7
Lab 8
Lab 9
Lab 10
Lab 11
Lab 12
Lab 13
76%
98%
56%
49%
71%
72%
66%
74%
43%
92%
52%
78%
273
400
169
71
279
207
357
313
33
100
166
61
300
Utilized Capacity (kW)
350
400
302
120
393
238
674
447
95
100
272
86
300
Available Capacity (kW)
$40,298
$42,904
$25,839
$4,000
$0
$16,288
$69,517
$34,740
$0
$49,494
$41,097
$0
$48,633
Maintenance Costs
$0.07
$0.07
$0.09
$0.09
$0.09
$0.06
$0.07
$0.05
$0.05
$0.05
$0.05
$0.05
$0.05
Electrical Cost (kWh)
$176,252
$248,784
$139,386
$58,063
$229,825
$116,311
$222,331
$142,426
$15,997
$47,917
$78,428
$28,685
$140,598
Total Electrical
| Chapter 3
100%
CRAC Utilization**
A summarization of high-level costs important for the business case UPS Utilization*
Table 3.2:
90 Assessing Value
88%
CRAC Utilization
83% 88% 55% 100%
UPS Utilization 75%
Lab 14
Lab 15
Lab 16
Lab 17
Lab 18
4,239
Total Capacity (kW)
Utilized Capacity (kW) 3,117
100
90
158
53
60
Available Capacity (kW)
100
50
139
44
54
Utilized Capacity (kW)
$422,132
Total Maintenance
$0
$10,438
$15,986
$1,700
$21,198
Maintenance Costs
$0.08
Average kWh
$0.13
$0.13
$0.10
$0.13
$0.13
Electrical Cost (kWh)
$2,040,448
Annual Electrical
$109,500
$54,203
$124,497
$48,116
$59,130
Total Electrical
*For office space environments that do not have a UPS installed, meter or sub-meter information can be used, but IT asset energy use will need to be estimated. **For office space environments that do not have dedicated air-conditioning, a cooling burden factor ratio can be used.
74%
63%
84%
74%
96%
90%
CRAC Utilization**
A summarization of high-level costs important for the business case (continued) UPS Utilization*
Table 3.2:
Presenting the Data 91
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The second model will be specific to the IT operation. This model includes all of the facilities attributes listed in the prior section but starts to align them to IT assets. The good news is that IT is not so logical in its designs that it can’t be aligned to building and departmental billing. What is often a challenge in aligning your assets is that, for many IT operations, asset management is a big challenge. This is exacerbated by the practice of many organizations to grow through acquisition. However, if you can start aligning groupings of IT assets with real estate, it will pay dividends in the future. You will be adding the following attributes to the IT operational model: Computing Both desktops and servers are typically tracked fairly well and, with some digging, can be aligned to buildings. Networking Network assets are not always tracked as well as computing assets, but similarly can be associated with computing and building. Storage Network attached storage (NAS) often falls under computing, but enterprise-level storage (tape or disk) will almost always be found in a data center. Tracking here can be a challenge with the various storage protocols. Nonstandard assets Some IT operations have a separate category for things like appliances: keyboard, video, and mouse (KVM) switches, and so on. Although these are not critical to track, in some situations you may be able to virtualize some of this infrastructure, so get it if you can. The IT asset data you gather will serve as the foundation of your IT modeling. Aligning this data to the data you gather from the facilities department will set you on your way to modeling the value that your program can bring. Table 3.3 shows an example of how you can align IT and facilities data into a format that can be modeled to assess value.
Table 3.3:
IT and Facilities asset alignment
Total Computing Load UPS Allocation CRAC Allocation Storage Asset Utilization
For most operations, computing is the largest consumer of electricity for power and cooling. What you will be showing IT is the next level of detail below the top-end consumption figures. This will be important in how you eventually structure your energy domains.
Floor Space Allocation Total Storage Load UPS Allocation CRAC Allocation Storage Asset Utilization
Storage by some accounts has surpassed computing as the largest power consumer of energy in IT. If it hasn’t already, the growth projections from many analysts indicate that storage will be the largest consumer of energy in the years ahead.
Floor Space Allocation Total Network Load UPS Allocation CRAC Allocation Networking Asset Utilization Floor Space Allocation
A distant third, networking infrastructure accounts for 10–15 percent of the total energy footprint of an IT operation. The energy requirements for networking are growing more quickly than computing but much slower than storage.
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Table 3.3:
IT and Facilities asset alignment (continued)
Total Other Load UPS Allocation CRAC Allocation Other Asset Utilization Floor Space Allocation Total Site Load By Data Center By Lab By Office Space Total Costs Computing Network Storage
Other refers to infrastructure such as appliances, KVM switches, door switch kits, and so forth. Although not much can be done today with this small subset of IT electrical load, monitoring it does bring some value. Many of these products can be virtualized, and understanding the energy requirements helps the case for doing so. After you have each domain of infrastructure broken out, you can begin to build your power map by site. Because there may be hundreds of sites to document, you are better off doing a small handful and developing a training module and tools to enable site owners to do it for themselves. After your power map is built, costs can be a simple overlay. Because power and cooling allocations are aligned to the different domains, you can also work with the facilities teams to identify areas that can be improved—especially labs and data centers that are overcooled.
Other Total CO2e
This is yet another overlay that can be applied to infrastructure, domains, operations, and buildings. This is simply a subset of wattage and costs. If there is an internal effort to reduce CO2, this can be a useful tool to show each organization their contribution.
Sharing Vision and Goals At this point, you are ready to pull it all together and formally propose a new program. The end state of this program is the adoption of a new service, but it starts with approval to run a proof of concept and then on to the pilot. Although you might be able to conduct a proof of concept individually, you will definitely need some level of support and investment to run a full pilot. Depending on your organization, you might be able to combine the proof of concept with the pilot. If you have lab teams that are interested in working on energy management, you should start there. Labs are typically not mission-critical environments, but still use a proportionately higher level of power than does office space. Targeting production data center environments on the first round is highly unadvisable. If you start with the lab teams, you will find there is as much interest in energy management for the cooling factor as there is in the cost savings. This is the first step into a whole new application area for IT, and we geeks love to work on what’s new. If you need to start with office space, it is certainly more likely than data centers to be supported; however, it has its own issues. We identified enhanced 911 (E-911) requirements that put many IP communications devices out of scope for the pilot of office space. We decided to leave the E-911 challenge to our engineering teams supporting the relevant standards bodies
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in the industry to work out. IP security cameras and any on-site storage were also out of scope. Desktops were the safest bet, and the technology in that space is already pretty well understood. Remember that in your pilot you will be actually turning assets off. You can safely assume that the team you’ll interact with the most and the one you really need to sell this to is IT. Facilities and finance groups will, in most cases, have a major say in how an energy management program is shaped. But IT is the group that will ultimately manage energy as a service (EaaS). After all, it is their assets that are currently using the energy. Your leastcomplicated value proposition and goal is simply this: IT has not had the drivers to directly manage its energy as an IT resource since the inception of IT. All we are talking about doing is implementing the same tools we’ve used for years to manage IT and applying them to the current challenges we face with energy. We are cleaning up our own backyard and can, in turn, teach our neighboring organizations to do the same.
Collaborative Success How productively you can collaborate throughout the development of the program will determine its success. We couldn’t understate the value of the WebEx, Google, and Microsoft tools we used to develop, track, and analyze our work.
How We Did It: A Passion for Green By the time we got to the pilot launch, we had 52 virtual team members actively contributing to the process. This was way more than was needed to do the technical basics, but we all had a passion to see the project happen. This was the part that we felt most comfortable calling Green, the sense we all had that we were applying our day job knowledge to a greater good. Leveraging this passion is not boundless. You need to show results in order to move ahead. We were also careful to respect the contributions of others, which can be a challenge when working virtually with a large team.
Although passion is important, so is recognition. Being sure to point to your sources and calling out good work will go a long way in building morale and commitment. Individual recognition is often a challenge when working in a virtual team model. Letting your work speak for itself is the approach we took, and it worked well. In the acknowledgments section at the end of the book, we highlight the contributions of our Energy Management Services Team members. In addition to team member recognition, departmental recognition is critical to moving past pilot to production. You can address this by brokering a discussion on electrical cost allocations between IT, facilities, and finance groups. You should seek to gain their individual support before hosting a larger discussion. The cost allocations will need to be determined in advance of a service adoption by IT in most cases. If you have the support of the functional teams, you stand a good chance of success. Working with them to build your program’s value proposition helps to ensure that it is tempered against business realities. The support of functional managers also lends itself well to gaining executive support.
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Executive Support It is now time to lay out your first formal executive-level presentation. At this point, your focus is on aggregation, interpretation, and summarization of the data you’ve gathered. You should be planning to take no more than 30 minutes with any of your executive stakeholders. If you can get them all together at the same time, for any amount of time, that is great. The reality is that you will need to conduct a number of one-on-one meetings. You can expect, at a minimum, to require support at the vice-president level in IT, facilities, and finance departments. There are some basic valuations you want to express at the executive level. The value you show to other teams will simply underpin these top-level roll-ups. In our case, we were developing the technology as we were deploying it, so it was bit different. However, the basic valuation matrix we positioned at the executive level is shown in Figure 3.2. In order to get executive support, a valuation matrix will likely be needed that shows what gains can be achieved using what approach.
Year 1
Year 2 1
Investment
Figure 3.2
2 IT Efficiency
Phase I – IT Assets POC and Pilot
Electrical Cost Reductions
3 Facilities Efficiency
Phase II – Building Assets POC and Pilot
Energy Management Phase III – Scale Out Production
Building Cost Reductions Facilities Operations
Energy as a Service Business Operations
Return
IT Operations
Year 3
Value
After you’ve positioned your value assessment of the many approaches to energy management, you should be in a position to request funding for your proof of concept and pilot. If Green is not a major priority for your company or is not yet well defined, it is better to lean toward a modest request. If your organization is interested in making Green actionable, go bigger. The strategy for your funding request will be developed as you go through your value assessment. You should be getting input all along the way on what will fly and what won’t. We opted to keep our program development highly iterative, meaning that we tried to keep our deliverables tactically simple, even with a fairly complex strategy. Our end goal was the formation of a new service, a big endeavor to be sure, but we worked it back from that lofty goal with simple, achievable deliverables. What we were able to show after three-quarters of development was a ramping down of wasted watts through our proof of concept and pilot, which enabled us consistently to get executive funding to support the program’s development (see Figure 3.3).
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Figure 3.3 First demonstrate a potential savings based on your activities and then reinforce your actual savings through regular executive communications.
Quarterly Reporting 1 IT Efficiency
Due Date: 12/2012 Status: 1.8.6 Total Savings: $2.1M ROI: 486%
2 Facilities Efficiency
Due Date: 12/2013 Status: In Planning Total Savings: TBD ROI: TBD
Due Date: 12/2014 Status: In Planning Total Savings: TBD ROI: TBD
3 Energy Management
Cost Reductions (kWh)
IT Asset Mix
Data Filler
1,200,000
IT Operations
1,000,000
Building Operations
800,000
Business Operations
600,000
Energy Services
400,000
Projections
200,000
Compute x86
Network
Applicance
Specialized Office
WLAN Storage
L4-L7
0 2007
Greenhouse Gasses 2008
Savings to Date: $2,168,504
2009
2010
2011
2012 1
Download Report
The Bottom Line Establish a system for your data that is conducive to modeling different value points. Data aggregation and modeling will be critical elements to developing your program. Whether you insert your data into another management construct or build a new one, data organization is crucial. Master It There is data on watts, cost, and billing periods in many locations across facilities, IT, and finance teams. How do you best structure this data for an initial assessment of the potential value your program will bring? Prioritize data valuation by business functions. Facilities, IT, and finance departments all have different interests to which you’ll need to cater. How you present the value of your program quantitatively and qualitatively will be important to getting your pilot funded. Master It All of the stakeholder groups you interact with will have some level of interest in the power, cooling, cost, and carbon data you mine. What are the priorities for each team related to these data sets? Model your data to build a modular value framework. Because you will be interacting with multiple stakeholder groups, there are multiple insertion points for the value your service brings. You will need modular data models that can be shared with multiple teams to garner support. Master It With business functions and organization incentives understood, you need to start mapping your value to the function’s value. How can you demonstrate the many points of value your program can bring in a context that is familiar to your stakeholders?
Chapter 4
Managing Your Project Now that you have structured your initial benchmarking and have begun to construct a value matrix that will shape your program, it is time to determine how you will pull your project together. To get started, you will likely need to build a new virtual team, provide recognition to individual contributors, manage their professional development, and structure achievements in developing an energy management program. The technology on which you’re focused is new and differentiated, and professional development is a natural outcome for those involved in this new space. Furthermore, the Green implications of your project should provide a level of personal satisfaction that motivates individuals to join your team. In addition to managing these team building considerations, you will need a good plan to “herd the cats” and get things done. In this chapter, you will learn the following: •u What methodologies and frameworks you can use to move your project from a pilot to a
program •u How to best aggregate and recognize the contributions of your virtual team members •u How to align resources toward common goals while keeping a realistic project cadence
Getting Started The first reality to accept when executing your project plan is that you will simply not have enough hours in the day to handle the business case, operations, technological development, and project management all simultaneously. Although you might be able to handle project management yourself in addition to the larger workload, your scope will need to be much smaller than what we’ve covered in this book. In other areas, you will need to collaborate and conquer in order to succeed. We covered in earlier chapters how to “sell” the facilities, IT, and finance departments on why they should be interested in supporting your project. After you get their “buy-in,” you will need to find project management resources to bring your team and their teams together in order to achieve common goals. This section assumes you can get support for project management internally or budget for sourcing this role externally. In our case, we leveraged Cisco’s internal project management resources within our Customer Advocacy teams. Regardless of where you go to get this help, you will need to direct the structure and pace at a high level initially. You will then need to stay on top regularly until you feel confident that your project manager (PM) is the right cultural fit and is fully competent. Think of a PM as if you were having a home built—you don’t need to see every nail being hammered, but you probably want to see the foundation and framing stages.
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| Chapter 4 Managing Your Project The PM you select should have experience equivalent to managing a large data center build out over a 24-month period. There are similar levels of complexity and risk involved with an enterprise-level energy management project. To help you find the right project manager, here are some qualifying points: Core Responsibilities Your PM will need to perform the usual project management tasks including scheduling, version control, project tracking, and meeting facilitation. Whenever possible, try to find a PM who already has experience in working with facilities and IT departments on complex issues. Additional Responsibilities Your PM doesn’t need energy-specific experience, but you should spend some time educating the PM on energy management. In some ways, the PM is the most consistent representative and spokesperson for your project for a period of time. It is important to make sure that the PM can clearly articulate project activities and goals using the right vernacular. Cultural Fit Although hard to quantify, having the right mix of personalities involved in a project is critical to its productivity. This is especially the case for a project in which you are bringing very different organizations together. Your PM should set the tone for collaboration and provide the right level of pressure to ensure delivery against goals. If you’ve not had much experience with this level of project management, be careful not to underestimate the level of effort required. A lot of nuanced debates will arise when you are focusing on energy. It is about as broad a focus area as one can tackle. Pile on top of that the major differences between IT and facilities departments, and you have a big, hairy, and audacious project on your hands. A good project manager can make or break your project.
How We Did It: Follow the Data Given the ubiquity of energy, our tactical discussions often quickly digressed into too much detail. Work with your project manager to establish clear parameters for project reviews. These parameters will remind the teams of the project’s level of detail. The detail and level of data quality you gather through your efforts will improve over time, and your PM should make this clear to all those involved. We have all seen many situations in which figures are debated in depth before instrumentation is even deployed. Your own energy will be better spent uncovering the data and following it where it leads you.
Drafting a Project Framework In order to get a PM assigned to your project, you will first need to draft a project framework. This framework will be your foundation for selling investment in the resources you will need to build a pilot. Your framework can include the following attributes: Project Goals These goals will differ from the vision of the project. The goals you articulate in your project plan should be measurable and achievable. Your goals represent what your project is seeking to accomplish.
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Methodology This is a high-level overview of your project’s timelines, activities, and milestones. This attribute is a fairly qualitative description of the how for your project. Activities This is a detailed description of the specific activities that will be needed to support the achievement of your goals. Level of Effort (LoE) You will need to break out the human hours based on the activities required in your project’s scope. Ownership Determining individual accountability in your plan is crucial to its success. You will need to name names to show who will do what to meet your project’s goals. Milestones Think of your milestones as the first level of aggregation for your activities. Milestones are the main accomplishments that will culminate in the successful completion of your project’s goals. Your milestones will be a commitment to when your program will deliver what. Timelines Your timelines are, in essence, a subset of your milestones and will enable you to estimate the total time requirements for the project. These are the logistical tools you will use to keep a steady cadence and to price out the cost of team member hours. Success Metrics You might not need specific metrics for each milestone. Some will simply be complete or not complete. However, for project phases such as your proof of concept, you will want to cite specific, measurable points that can be logically extrapolated. For example, your pilot can target an operating expense reduction by using the success metric of a percentage reduction in kWh cost over a three-month period. You might not need to provide all these attributes simply to secure a project management resource. However, in most cases you will need to articulate your project goals, methodology, and activities. These points will enable a project manager to estimate his or her hourly requirement to manage the project. This LoE scoping for project management will be the first of many LoE assessments you do throughout the project’s life cycle. Table 4.1 shows an example of a simple spreadsheet framework you can use to get this process started.
Table 4.1:
A basic pilot project framework
Deliverables
Track Description
Activities & Level of Effort
Operations
What is the breakdown of your company’s current energy use by business operation? For example, how much energy is currently used for lab, data center, and office space, and by what function?
1. Mine existing data. 2. Analyze data and normalize it. 3. Develop a new comparative model. 4. Deploy instrumentation if possible.
Buildings
Which buildings use what types and amounts of power? Consider leased buildings as a second-phase effort after you’ve addressed the buildings that are owned outright.
1. Mine utility bills. 2. Separate owned versus leased properties. 3. Align building energy information to operations data.
Track 1: Current Cost Allocations
Benchmarking
| Chapter 4 Managing Your Project
Track 1: Current Cost Allocations
Table 4.1:
A basic pilot project framework (continued)
Deliverables
Track Description
Activities & Level of Effort
IT Assets
You should be able to credibly estimate energy use of each IT asset hardware platform by mining existing data. Your program will work toward providing energy information by individual assets.
1. Mine existing asset/inventory lists. 2. Analyze data and normalize it. 3. Develop new comparative models. 4. Deploy instrumentation if possible.
Facilities Assets
What facilities assets consume what types and amounts of energy? This can be left to subsequent phases if not readily available. Burdenfactor analysis will suffice for initial benchmarking.
1. Mine existing asset/inventory lists. 2. Analyze data and normalize it. 3. Develop new associative models. 4. Deploy instrumentation if possible.
Users & Administrators
In future phases, you will want to benchmark individual infrastructure administrators on their energy use. With some simple associations, you can do this today.
1. Align existing data to managers 2. Provide team roll-ups
IT Operations
What is IT’s ROI, if any, under the current system? Is a theoretical ROI attractive enough for IT and facilities departments to change billing models?
1. Align IT asset data to IT operations. 2. Assess IP-enabled energy technologies. 3. Choose domains of focus. 4. Estimate savings over adoption cycles.
Real Estate Operations
What is the ROI under the current system for the facilities and real estate teams? Are they willing to invest in IT skill sets to achieve an improved ROI model?
1. Estimate IT/facilities savings correlation. 2. Check your savings estimates against existing real estate data. 3. Model savings potential against new costs.
Business Units
Under a pass-through billing model, the business units should have an interest in knowing a specific ROI. Although it will be based on estimations, a plan to move toward asset monitoring will continually improve data accuracy.
1. Model operations, assets, and buildings. 2. Align asset energy allocation estimates to current IT service billing data. 3. Estimate cost for automated reporting. 4. Consider integration through finance teams.
Business Case
Track 2: Savings Potential
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Track 2: Savings…
Table 4.1:
A basic pilot project framework (continued)
Deliverables
Track Description
Activities & Level of Effort
Industry & Government Incentives
Many utilities and local municipalities offer rebate programs that can provide additional savings opportunities.
1. Research Green incentive programs. 2. Research energy incentive programs. 3. Include incentives in your net business case.
Human
Is the potential for human error increased by deploying energy management?
1. Mine existing data on failure causes. 2. Find comparative service models (such as VoIP). 3. Provide best estimates on human error.
Physical
Does energy management increase physical security (access) risks?
1. Align program and energy services plan to existing security strategy and identify any threats to network security.
Logical
Does energy management threaten critical systems’ uptime? What steps can be taken to mitigate any threats?
1. Align program and energy services plan to existing security strategy and identify any threats to physical security 2. Identify any threats to uptime due to electrical grid supply availability. 3. Identify any anticipated threats to uptime due to human error.
Financial
Can the current risk profiles be assessed in financial terms? If so, what is the potential cost related to increased risk?
1. Assign credible costs to any areas of risk.
Track 3: Availability
Risk
Track 4: Program Development
Solutions Architecture Applications
What software platforms will provide the monitoring, management, and reporting of energy use? What are application dependencies across architectures?
1. Assess energy management technologies. 2. Short-list and prioritize platforms. 3. Quality-check your short list against scalability.
IT Architecture (IP-enabled)
How do the current management domains interface with each other? How does energy management complement or compete with them?
1. Host workshops to reconcile architectures. 2. Assess the integration’s level of effort. 3. Assess costs to in-source integration.
| Chapter 4 Managing Your Project
Track 4: Program Development
Table 4.1:
A basic pilot project framework (continued)
Deliverables
Track Description
Activities & Level of Effort
Integration Points
What is the work required to integrate into existing platforms? Are there opportunities to consolidate?
1. Develop a structured integration plan. 2. Highlight consolidation opportunities.
Facilities Design (MEP)
Can the current mechanical, electrical, and plumbing design support more-dynamic energy requirements? Can the current building management systems provide enough detail to associate facilities assets with IT assets?
1. Host workshops to assess facilities availability. 2. Identify areas of high risk and segregate them. 3. Develop standard operating procedures. 4. Develop a network mediation plan. 5. Integrate IT and facilities road maps.
Operations
Are the current facilities and IT operations staffed to support energy as a new service? What skill sets are needed, and how much will it cost to acquire them?
1. Assess the current skill base within IT and facilities departments. 2. Assess the current incentive structures. 3. Assess the current cost allocations
Human
Who will manage energy as a service for the company?
1. Develop a management team development plan. 2. Research remote management options.
Software
What software platforms, applications, and systems will be used to manage and report on energy?
1. Choose appropriate technology partners. 2. Begin structured integration.
Hardware
What infrastructure will be managed, and who is responsible for administering changes to assets that can’t be managed or consolidated?
1. Develop internal management standards. 2. Document current state compliance. 3. Develop a structured migration plan. 4. Choose appropriate hardware vendors. 5. Initiate consolidation initiatives.
Management
Track 5: Operations
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In our case, we did not need to build our framework from scratch. Instead, we leveraged the project frameworks from within our professional services teams. These frameworks needed to be tweaked slightly, but using them saved us from having to reinvent the wheel. If you do have access to existing frameworks, target the ones used for larger projects. Energy is arguably the most “horizontal” IT service. Thus you will need to embrace a broader project scope than the typical IT service.
Methodology At the highest level, your methodology is iterative and achievable. Although the quality of energy services has ramifications for all other services, you can still work to keep your project and program relatively straightforward. In our case, we were building the technology as we deployed it. Some elements of energy management services development relate to efforts to integrate existing systems, but for the most part, you are focusing on the business case, technology assessment, proof of concept, and pilot. The next level down from a practical, iterative approach relates to how you approach the checkpoints for your project. In our case, each checkpoint was based on either improvement against benchmarks or fulfilling user requirements for new features. For benchmarks, we simply compared them against our proof of concept and pilot. As for the end-state magnitude of savings, we were not overly focused on that. Savings are scalable, and your initial savings might be small but will expand as the service matures. Finally, we approached the teams (facilities and IT) that would eventually manage energy and gathered user requirements. These requirements were integrated throughout the project to ensure that the eventual program managers would feel a sense of ownership.
Note When gathering user requirements from the teams that will eventually manage energy services, be sure to capture them in way that can be easily interpreted later. For example, one requirement might be to bill energy at the application level and across virtualized computing resources. Although application-aware energy billing is still a long way off, you can take steps today to employ flexible metrics such as basic wattage per unit of work that can be used later for more sophisticated energy cost allocations. Because you will be benchmarking against multiple tracks, you will need a scalable framework for measuring your success. For our pilot and proof of concept, we simply used spreadsheets with different worksheets for each area of measurement. We sliced the data we uncovered across buildings and operations to develop a power profile. These basic profiles enabled us to analyze power costs over time throughout our project methodology. Multiple data filters will be required to demonstrate value across IT, facilities, and finance departments. We cover this in greater depth later in the chapter. The next level of detail that you will need to cover through your methodology is the assets across facilities and IT departments. The assets themselves are your functional planning, billing, and architectural elements. It is at the asset level that savings will be achieved if you plan to adopt an IP-enabled energy management approach. The scope of coverage for assets in your methodology can include the following: Production Electrical Production refers to the productive work an IT asset would require to perform a particular function or set of functions. One of the goals for your project will be to define this for groupings of assets. These groupings are referred to in later chapters as energy
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| Chapter 4 Managing Your Project domains. Given that the instrumentation capabilities in the industry don’t provide a common workload correlation by asset, initially this will need to be an estimate. In our case, we estimated production requirements based on function, time of day, and dependencies. We later used the power bell curves we uncovered through monitoring deployment to quality-check our time-of-day estimates. Not Currently Productive Electrical This is the difference between production power and total operative power capacity. This attribute refers to the time that IT assets are idle. Many IT asset platforms such as switches, servers, and disk-based storage arrays can require as much as half of their energy requirements while idle. This nonproductive time is sometimes referred to as vampire power, and it is what you aim to eliminate. This is perhaps the most key area in terms of defining what efficiency means to your IT operations and where you should spend much of your time—eventually. The breadth and scope of IT systems is extremely diverse. That is not to say that a high degree of energy efficiency can’t be achieved as a standard. Instead, it means that too much time spent on the theory of energy-efficient IT can detract from the basic need to instrument commonly first. Common instrumentation on open platforms will lead to user-defined control and energy-domain administration. Ultimately, you will need to define your own notions of efficiency across the workloads and infrastructure architectures that are unique to your organization. Facilities Asset Profiles Because UPS and CRAC units are almost entirely reactive to the IT load, you can simply include facilities assets as always productive in your first round of analysis. The efficiency of these systems often mirrors that of IT as well. If these assets are supporting lower loads, they will in turn run less efficiently. You can break out this data later as you approach IP-enabled monitoring and control of these assets. Thermal Profiles After productive and not currently productive electrical loads are determined, the heat removal requirements for each can be estimated by using a simple burden factor for critical facilities assets. Asset Criticality Tiering Eventually, you will want to deploy your program into data center environments. Before you do this, you will need to associate applications (and their dependencies) to assets. This is a necessary step before you approach control of mission-critical assets. Including this data in your initial scope (even if it’s for office space and labs) sets the stage for subsequent phases and gives you the time to get through a lengthy applicationprofiling exercise. After you have your framework in mind and have your methodology articulated, you should be able to work that methodology into the scope that you started with for your initial benchmarking and business case. This sets you on a path toward making the case to secure a professional project manager.
Project Cadence The safest approach for your project will be to set conservative timelines for deliverables. It is easy to be overly aggressive in setting delivery dates for a big project like this. It’s important to remember the difference between selling the initial investment concept and knowing what can be actually delivered. In our case, we took our time and were careful not to let the “tail wag the dog” for our proof-of-concept and pilot phases.
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Some situations might require more expediency to meet budget cycles, social popularity, and organizational changes. If you do need to move quickly to capture a window of opportunity, keep these basics in mind: •u First, don’t forget to scale (at any phase) through virtual teams. As John Heywood said,
“Many hands make light work.” •u Second, be sure to prioritize based on impact. It might be more effective to integrate ele-
ments of your program into existing, well-established programs initially. •u Finally, keep a regular line of communications open with all your stakeholders. Some
internal marketing will go a long way and can be as simple as regular, well-authored, and informative emails. Ultimately, your PM will own the project cadence. As program lead, you will need to oversee the main milestones to be sure they are realistic. Part of this process will be working regularly with multiple virtual team members. A good practice is to set up regular strategic reviews of the project’s timelines. The strategic planning of how the program develops might not be covered by your PM, but can influence your delivery dates. Keeping the strategic direction clear, relevant, and timely across the many teams with whom you are working will ensure a successful project pace.
Building a Team True motivation comes from achievement, personal development, job satisfaction, and recognition. Frederick Herzberg At this point in your program’s development, you can begin to partition your stakeholders from your functional team members. This is where the real, functional work begins that will require tangible support, both human and financial. Through your benchmarking efforts, you should get a sense from the facilities, IT, and finance teams as to the popularity of your concepts. This is your first feedback point to consider when thinking about who to ask for project team support. Balancing stakeholders and contributors will be important to your project’s overall success.
Note When separating your stakeholders from your functional team, be sure to remember who is the client and who is the vendor. Although not necessarily formalized, you are the architect and program manager of a new service. In this analogy, you are the vendor. For IT, which likely does not pay the power bill directly, jumping to a new service without a proof of concept and pilot is probably a nonstarter. Facilities and finance departments might be more receptive to a more formal exploratory project. You should anticipate that you might need to complete your proof of concept and possibly your pilot without direct support from IT. The structure and skill set of your team will be predicated on the scope of your proof of concept and pilot. If you follow our example and focus only on IT assets for the first phase of your program, you will need primarily IT skill sets. To bring together the team focused on the largest power-consuming domains of IT assets today, you can refer back to your benchmarking data. You need to focus on whoever are the largest consumers. As you get through your asset-level benchmarking, you will uncover what energy goes to what platforms. If you’re big on servers
| Chapter 4 Managing Your Project and storage, then bring these skills into the mix. If the network looks to be small in terms of consumption but large in its platform potential, bring in network managers. The team will form around your program’s goals. Although all industries have a trend toward more digital enablement every year, the mix will vary slightly. Be sure to use the 80/20 rule here. Find the top 20 percent of platforms (hardware and the software it supports) that drives 80 percent of your energy use. You then work with the domain managers of these platforms to determine what can be done and when. These domain owners (typically computing, network, storage, and facilities) may or may not be part of your project team. The team’s makeup will vary on a case-by-case basis and will depend largely on what cycles and interest a particular team has. If possible, try to partner with these teams to spread the workload, but if not, look at them as your customers to whom you can provide energy reporting and control. In our case, we found that a good, rounded group of skill sets worked well for us. Generally speaking, we built a team with roughly 80 percent of team members having traditional IT skill sets (mainly network management) and 20 percent having skills in data center facilities design and management. We found the data center facilities skill set preferable to real estate management skills for architectural and operative considerations. Facilities professionals from the data center space are typically the most IT conversant individuals and should be a good cultural fit. The network management perspective was key in that we needed “cross-domain” rather than proprietary and “silo’d” thinking. Table 4.2 shows an example of how to build and track your team, their skills, and domain ownership. You can simply align Table 4.2 to Table 4.1 and add in names as you fill any skill gaps. As program manager, you should have a baseline familiarity with all of these skill sets and recruit individuals who are versatile and deep in multiple areas.
Table 4.2:
Form follows function from pilot to program
Skill Set Requirements
Track Focus
Benchmarking
Track 1: Current Cost Allocations
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Financial Accounting: Basic accounting skills with some financial analytics.
Operations & Users: Much of the financial accounting will be done by you. However, you can gather data from your financial office covering cost allocations, departmental billing, historical costs, trending, and corporate initiatives. Look to integrate accounting into the financial operations of your company.
Energy Accounting: Basic electrical and mechanical skills focused on real estate.
Buildings: The ability to expose costs related to changes in energy usage across a range of environments will be integral to your program. If you do not have a background here, self-study and/or partnership with a facilities professional will be necessary for your program. You can most likely partner with a facilities counterpart here.
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Track 1: Current Cost Allocations
Table 4.2:
Form follows function from pilot to program (continued)
Skill Set Requirements
Track Focus
Asset Management: Existing, integrated skill set within IT and facilities operations. Unique to each operation.
Facilities & IT: You should be able to credibly estimate energy use for each IT asset hardware platform by mining existing data. Your program will work toward providing energy information by individual assets.
Marketing & Communications: Basic communications and presentation skills for project proposal development.
Project Development: You will need to communicate your value concisely and consistently throughout the life cycle of your project and program.
Tracks 2 & 3: Cost & Risk
Analytics Financial Modeling: Moderately advanced accounting and business financial modeling.:
Cost Allocations: The project, pilot, and program will need to model changes consistently across real estate, critical facilities, and IT architectures. These skills are often found in data center teams.
Risk Analysis: Moderately advanced corporate and business risk assessment.
Security & Uptime: Given that energy is the most critical service for the business, you will need to demonstrate credible risk mitigation and security compliance. For the pilot, this level of analysis is less critical, but for your actual program it will be critical.
Track 4: Program Development
Implementation IT Architecture: Advanced logical and physical architecture across network, server, and storage.
Systems Architecture: Whether your approach is fully integrated or partitioned, you will need to document the technical specifics of your pilot and program. This is the most important and differentiated contribution your program will bring by applying IT systems to energy management.
Facilities Management: MEP design specification and critical facilities management.
Facilities Support Model: In order to mitigate risk and achieve maximum savings, you will need full alignment with the systems that support IT. The program you create can have a significant impact on facilities operations. Integrating a facilities management function into the program is highly preferable.
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Track 4: Program Development
Table 4.2:
Form follows function from pilot to program (continued)
Skill Set Requirements
Track Focus
Database: Basic database administration with a focus on data normalization.
Tracking & Reporting: Capturing benchmark, pilot, and programmatic data is critical to your success. Ensuring data quality is consistently improved to a target-accuracy goal might dictate a dedicated database administration resource for a period of time.
Systems Integration: Data-center-level systems integration across computing, network, and storage.
Pilot Implementation User interfaces, connectivity, rack and stack, and all staging requirements will be needed to run a pilot. For program-level deployments, additional resources may be required to scale.
Program Establishment
Track 5: Operations
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IT Management: Senior IT infrastructure management and senior systems management.
IT Operations : The top-level management for this service will be within IT and can be integrated into the existing services portfolio. It will be up to the managing organization to determine the skill sets required based on the scope of management.
Real Estate Management: Senior automated real estate management.
Building Operations: In rare cases, a real estate operation may want to incorporate your program as a standalone, IP-enabled function. In this case, building management and automation systems might be leveraged more than traditional IT constructs.
Virtual Teams Perhaps the best way to scale in larger organizations today is through a virtual team model. This model enables you to spread the work over a larger resource base by asking for percentages of people’s time. As you go through the scoping of the pilot and program, you will be breaking out a level of effort as man-hours. Once tallied, these man-hours can be mapped to the required skill sets, and your virtual team is born. Without a doubt, the biggest project challenges to a virtual team model are related to coordination and collaboration. The more you subdivide the man-hours, the more complexity you introduce. There is a fine line between being effective and being spread too thin. Sound project management and effective collaboration tools will mitigate these challenges. Regular, clear communications will also help keep your team on the same page while giving you a podium to communicate obstacles. Managerial support will, of course, also be critical to the success of your efforts. You will need to demonstrate the value of your project before requesting formal contributions from a team member. You will need to understand the priorities of the team and present your value to the team’s manager to get the support you need. When you do this will depend on when you
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need a particular skill set to meet a deliverable for your project. In our case, we always tried to approach the functional contributor before approaching their management. Otherwise, you can give the impression that you are signing people up for something they don’t want to work on.
Management Visibility There is nothing that says you have to highlight the contributions of your virtual team members. If management is sold on contributing to your project, that might be enough to get the job done. However, a bit of morale building goes a long way here. Where possible, we made it a priority to highlight individual contributions back to direct managers and to the broader virtual team. This instills a sense of teamwork and accomplishment that benefits everyone. It also demonstrates your integrity as the program lead. As mentioned in earlier chapters, regular executive communications are important. This applies as much to your pilot as to your program. You need to show a steady cadence of delivering value. You might also consider tailoring your communications to the specific stakeholder group rather than sending a generic email. We developed three separate newsletter-styled emails and one major project-update correspondence. This enabled us to speak to our value in a relatable context across different organizations. In terms of visibility for your team members, you can also translate their individual value to groups outside their own. In addition to this management best practice, you will also be subtly advising your executives on what skills will go into managing energy as a service. If you are lucky enough to have a senior executive who has a personal or professional passion for Green and energy’s role in it, the corporate communications function might be simpler. Any business function will typically have an executive communication, and you might be able to integrate your updates into an existing vehicle. One or several executive communications managers will save you time in creating newsletters, charts, and graphics to “market” your value. In the spirit of scaling virtually, be on the lookout for a function such as this that you can bring on board to help you consistently demonstrate your value.
Aggregating Resources and Execution Another common challenge in scaling through virtual teams is how best to aggregate, align, and logistically manage a particular effort or function. This is where virtual teams become very black and white. Most either run their course for a finite project or become mature enough and valuable enough to warrant a codified business unit. The difference between the two scenarios often comes down to execution. This section will help you personally scope the effort required to execute against your goal while highlighting some tools and methods that helped us achieve ours. A good rule we have found for estimating your own hourly commitment to aggregating team resources and driving execution against goals is to double whatever time you think it will take you initially. Miscalculating your own workload for basic, logistical management translates into a lot of evening and weekend work. We estimated that the time we spent on this throughout pilot to program development was more than 30 percent of the total project hours. This would have been more if we didn’t work at Cisco and have access to some serious productivity tools such as WebEx Connect, Google SketchUp, Google Earth, and Microsoft Office. The takeaway is that the logistics can be a black hole if you don’t plan for them because you’re the one who has the primary execution responsibility.
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| Chapter 4 Managing Your Project The methods you employ in managing the contributions to your project are priority one. Your methodology here differs from basic data aggregation to human resource management of the many teams involved. Think of it as managing a team that has no direct accountability to you as the manager. The following are are some good rules of thumb that will help you effectively facilitate contributions in a virtual team model: Make It Easy Although this means more administrative cycles from you to organize and polish the teams’ inputs, it will ensure that you meet your goals in a timely manner. Make your content easy to read and organize it at multiple levels of detail. This will ensure that people can readily interpret the value that your effort brings. Standardize Spend some time mining or creating a new a set of templates for all the relevant content that is contributed. The types of applications provided by Google and Microsoft Office should be all you need. Centralize Keep all your content in one place that can be accessed through the Web. This is where you can maintain revision control and save yourself time digging through your file folders and emailing documents one at a time. Making effective use of a central repository saves you time on communications, content, and project deliverables tracking. Align Any function can prepare roll-ups and report on progress. Take the time to step back and look at the strategic implications across business functions. Call these alignment points out as areas that can be optimized. There is likely no function looking at alignment across facilities and IT today, but there is significant value in aligning disparate business functions toward the common goal of reducing waste. The dynamic alignment of related but partitioned priorities will be among your top contributions. At this point, you are moving past a please take a chance on me posture to one that says, we have a plan that we need to execute. You will need to walk a fine line between encouragement and enforcement. Because you might not have the authority to do the latter, when possible take the time to educate, aggregate, and align the many points of view you encounter. The technology will take you only so far; it is the alignment of the technology and its juxtaposition to operations that will help provide your greatest value. Energy management is quickly becoming less about the technology and more about the organizational disruption that comes along with it. The rest of this section discusses what we did to mitigate this disruption and aggregate, align, and employ successful tools toward a pilot deployment project.
Facilities Teams Although there are many exceptions to the rule, most facilities and real estate departments are reactive to user requirements. These teams look to organize their data primarily based on billing and capacity-management constructs. Where possible, request data on building and facility asset power usage in its native form. This will save the facilities teams from having to interpret and change any data sets. You can take this raw data and translate it into a variety of usable forms. As you start to gather data on buildings and facilities in parallel with IT asset energy information, you can form the associations between IT and facilities infrastructure. These associations will help you estimate the savings you will get from cooling systems’ thermostatic controls. This will also help you identify opportunities to consolidate facilities assets that are
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underutilized. You can then issue any content you create on associations, capacity, and savings estimates back to the facilities teams who are supplying the raw data. The project-level reporting you provide back to facilities teams will help you drive them toward your execution plan. Generally speaking, your execution plan with the facilities department is aimed at getting them to make iterative improvements to the supporting facilities infrastructure. You do this based on helping them to track and report on IT improvements and contrast these improvements against risk and cost.
IT Teams Most of your work with IT in this build-out phase of your project will be asset based. You will be the aggregate point for allocating energy usage to the many assets under IT’s management. It is fairly common to see more than 1 million IT assets for a company of 50,000 employees. As you go through the asset and inventory lists, you will find that many of them can be immediately eliminated, because up to 20 percent of these assets will not require power. This will be your first asset scrub: removing things such as racks and cable trays from the scope of management. Although not yet broadly deployed, some technologies on the building management side such as the Green Building Extensible Markup Language (gbXML) protocol can be an excellent data source. The gbXML schema provides a critical physical-mapping capability that exposes an asset’s location within a building. We cover these emerging technologies in the final chapter. As you begin to group the many assets for the company, you will need to provide multiple filters to the assets. The filters we employed accommodated availability, function, application support, business unit support, total wattage, asset type, and part numbers. Table 4.3 shows an example of how you can structure an asset-tracking matrix. After you have the assets grouped in a particular way that is most meaningful to your operation, you can start to expose the data to the teams. This might be a surprise to some domain owners because this is probably the first time they’ve seen how much energy their servers, switches, or storage arrays use. Making this information visible will be the first step in getting IT behind your execution plan. The plan with IT is to prioritize assets, gather the data points needed to model shutdowns, and identify opportunities for limiting wasted watts.
Project Milestones We structured our milestones based on the data we uncovered. One of the early challenges we faced in getting support for the project was the fact that the plan was initially built on a lot of hypothetical assumptions. This was simply because there was no precedent for this scale of energy management. However, we could show clear iterative development through our project plan. The time you spend developing a solid project plan with achievable milestones will be well worth it. These milestones will also serve as the points on which you will provide updates through your communications plan. The project plan often does not get translated into a good communications vehicle. It will not help the communications with your teams to provide updates on your project’s milestones in summary form. A good slide deck or newsletter will do this. Remember to stick to a common format as much as possible and use bullets and graphs for the highlights. Showing a good pace and progress against milestones will help to ensure that the investment in your program scales upward.
All assets tagged with storage in a building 7 energy domain.
Storage
All assets tagged with switch, route, phone, WAP in a building 7 energy domain.
Networking
All assets tagged as computing in a building 7 energy domain.
Computing
Infrastructure
1,000 W
350 W
1,000 W
Total Energy (Watts) This field gives you a rate of consumption for an electrical capacity over time. Watts per hour can be expressed as cost based on local electrical rates.
Tape Disk SAN array NAS JBOD
Switch, Router, LAN switch, SAN switch, Optical routing
Desktop, Laptop, Server (x86), Server HP-UX
Asset Type This field enables you to group and model classes of infrastructure. You should use a consistent naming convention.
EMC RAID CL123XA
Bay Networks BN123XA
Apple iMac 123XA
Model & Part Number This helps you tie in your data modeling with existing asset management systems.
Filtering data across assets to build new analytic models
Assets Naming conventions will be very important if you plan to manage energy in a cloudbased model. Documenting a naming taxonomy is important.
Table 4.3:
Energy Domain: example.building
Labs Data Center
Labs Data Center Sales Marketing Finance
Labs Data Center Sales Marketing Finance
Business Unit Asset ownership and access will be needed throughout your modeling and program.
99.99 percent
99.99 percent
99.99 percent
Availability Typically expressed as a percentage of electrical availability over a period of time. May be an estimate up front, but will be actively tracked later.
45 percent
60 percent
25 percent
Capacity Utilized Represented as a percentage of utilization over time for a given workload unit of measure. Not critical up front, but allows for reporting integration later.
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Energy Domain: example.building
UPS CRAC Switch Mechanical Transformer Genset
Office Lobby External Emergency
Printer WAP Telecon Displays Access Appliance Vending Kitchen
4,900 W
1,800 W
180 W
All assets tagged with other (printers, digital signage, and so on) in a building 7 energy domain.
Other
All assets tagged with lighting or lighting controller in a building 7 energy domain.
Lighting
All assets tagged with MCF (missioncritical facilities) in a building 7 energy domain.
MCF
Asset Type This field enables you to group and model classes of infrastructure. You should use a consistent naming convention.
Total Energy (Watts) This field gives you a rate of consumption for an electrical capacity over time. Watts per hour can be expressed as cost based on local electrical rates.
Telepresence TP123XA
SQD controller SQ1234XA
Silicon UPS SL123XA
Model & Part Number This helps you tie in your data modeling with existing asset management systems.
Labs Data Center Sales Marketing Finance Custodial Contractors Security
Labs Data Center Sales Marketing Finance
Labs Data Center
Business Unit Asset ownership and access will be needed throughout your modeling and program.
Filtering data across assets to build new analytic models (continued)
Assets Naming conventions will be very important if you plan to manage energy in a cloudbased model. Documenting a naming taxonomy is important.
Table 4.3:
45 percent
63 percent
99.99 percent
Availability Typically expressed as a percentage of electrical availability over a period of time. May be an estimate up front, but will be actively tracked later.
45 percent
N/A
65 percent
Capacity Utilized Represented as a percentage of utilization over time for a given workload unit of measure. Not critical up front, but allows for reporting integration later.
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| Chapter 4 Managing Your Project Finally, regular updates to the master document on progress can gobble up many of your own cycles as the project owner. Selecting the right project management staff and using multimedia tools can help ease some of this logistical work. After you get individuals actively contributing to the project, you can assign ownership to different tracks. Part of the deliverable for a particular track that supports a particular milestone can be to update a common project management template. In our case, we used a combination of WebEx and Cisco proprietary project management tools. Both tools enabled us to structure a project in which each track and responsible delivery team could self-administer toward meeting the project milestones. Coming together is a beginning. Keeping together is progress. Working together is success. Henry Ford
Getting Organized You’ve arrived at the point where you should feel confident that you have all the structural and programmatic elements mapped out. Using the series of benchmarks you’ve documented, you can move ahead on the project plan to improve the current state. As you read succeeding chapters, pay careful attention to the work required to keep the project on track and properly scoped. There is a reason professional services organizations get paid well for project management: It requires a lot of work. If you have any concerns at this point about your available cycles to manage a large project or your skill set in approaching it, get some help. You should be able to move a business case and project scope forward to the point where you can get project management resources assigned from within your own company. If this function does not exist or you can’t get a high enough priority, you should look to set a budget for external project management resources.
Getting Together Use the collaboration and project management tools that are out there. Many of them are free if your organization doesn’t already have them. Collaboration tools help your project team get together and meet the goals you set forth as a team. As the project lead, you are responsible for keeping everyone informed while driving toward a common end state. If you communicate the progress of the project and the contributions of the team quickly and effectively, you will be well on your way. Developing a vision or mission statement such as the following for your project team might help: Our project team has been formed to assess the business case, people, process, and technologies behind an energy management services deployment. Our goal is to provide incremental savings through the reduction of nonproductive energy losses. We plan to do this by deploying new IP-enabled energy management technologies.
Structuring Your Success The project methodologies, structure, and scope we used might not fit your operations exactly. Take what we’ve laid out in the first four chapters as a guide, not as a blueprint. Up to this point,
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we have covered the foundational elements you will need to devise a successful project model for your operation. Don’t worry about changing things to suit your particular needs. Moving forward to Chapter 5, “Building a Pilot Deployment,” and Chapter 6, “Pilot to Production,” we will approach the technical elements that will ultimately influence your project’s scope. There will be some back and forth in your project planning as dictated by technological capabilities. Given that our approach is IT driven, you should anticipate a steady pace of new technologies that can alter your project’s direction. Where possible, try to keep your project structure flexible enough to accommodate new, compelling technologies.
The Bottom Line Scope a proof-of-concept deployment and assess the core technologies that will support your pilot. You will need to have a basic understanding of how to scope a project in terms of the technology you will use and the skills you will need to administer it. Master It Map one to three technology solutions to each set of benchmark data as having a potential to reduce wasted watts. Based on the published savings for each technology, develop a savings target for a suite of solutions built within and across the different technology options. Effectively manage collaboration. You will need to set up and maintain a collaboration space for your project team to aggregate and track content and progress. Master It If you are already using a good suite of collaboration tools internally, it probably makes sense to stick with them. Using a tool that is already familiar to your teams is ideal. Establish and maintain regular communications. You will need to establish a communications plan to keep your project team and stakeholder groups informed and working toward common goals. Master It Look around the organization to see whether there are any marketing resources that can contribute to your efforts. These teams can take your raw content and polish it to the point that it is clear, concise, and professional in appearance.
Chapter 5
Building a Pilot Deployment The Lewis and Clark expedition was the first overland journey to the Pacific coast and back undertaken by the Unites States government. The goal was to assess the resources and land obtained in the Louisiana Purchase. After the expedition, the data collected and the routes taken were used as the basis for the expansion into the West. This is a fitting metaphor for a pilot, especially for our project. Every new technology venture goes through the same process. Over the years, it seems that every new project we’ve started required some technology or some software that simply never existed before. We’ve come to realize that, with any project, you not only have to accomplish the task at hand but you need to be able to build the tools necessary for the task and set out on a discovery of the new area. You need to set out and discover the things you didn’t even know you needed to know. We’ve nicknamed this Lewis-and-Clarking. If you look at the phases of that journey in history, you can see a pattern for exploring new areas. The steps to discovery, pioneering, settling, and maintaining are always similar. They apply very nicely to your plan of attack for creating an energy management pilot. In this chapter, you will learn the following: •u How to select an engineering team for your pilot program •u How to identify a mission and philosophy for your pilot •u How to select a pilot energy domain •u How to identify the basic data you need for your pilot system •u How to inventory the systems you will deal with and find out how to access the data
Understanding Energy Management Before we embark on a new energy management pilot, it is useful first to go over the current state of energy management. Today energy management is roughly at the point where network management was in the early 1990s. As IP networks expanded, hardware vendors and software companies were all coming up with ways to provide network management. The same expansion for energy management is occurring now. A buildup of technology innovations in related fields is causing a change in how you can manage energy. Network and computer systems management existed long before the Internet, but changes in technology caused a change in how the managers operated. The same changes are now affecting how energy is managed.
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| Chapter 5 Building a Pilot Deployment FCAPS In the network management field, vendors and network management providers use the FCAPS model as their guide. FCAPS is the International Organization for Standardization (ISO) telecommunications model for managing networks. FCAPS stands for the following: Fault Handling of events that recognize, isolate, correct, and log errors or notifications that occur in the network Configuration Handling the change of settings or provisioning of devices Accounting Handling the billing, share quotas, or administration of devices Performance Handling the efficiency availability and throughput of the network Security Handling the control of access to devices FCAPS areas were defined in the 1980s. If you look at them, you will see something that is also missing from the cars built in the same period—there is no area that deals specifically with energy or power consumption.
FCAPS + E The Performance and Configuration aspects of network management deal with parts of energy management, but nothing calls it out as a discipline unto itself. If the FCAPS areas were defined today, there would most certainly be a separate category for energy management: Energy Handling the consumption and control of power on the network That would mean expanding the acronym to include energy: FCAPS + E. For your pilot, you’ll be looking at only a portion of the FCAPS + E areas. The areas to focus on are Accounting, Performance, and, of course, Energy. If you look back at the exploration metaphor, you wouldn’t expect your first expedition to start building settlements. So you’ll be looking at a portion of a complete plan. Essentially, what you want to do for the pilot is to define a simple energy management system that can account for devices and then monitor the power usage. Until you have a complete system in place, your pilot will be concerned with only the following: •u Defining an area of energy management within your organizations •u Finding and inventorying devices in your organizations •u Categorizing and classifying devices •u Measuring the power usage of devices •u Reporting the usage
Chapter 6, “Pilot to Production,” and Chapter 8, “Administering Energy Domains,” go into more areas of energy management. Those chapters discuss topics such as the following: •u Being notified of out-of-bound situations on power usage •u Configuring policies and rules regarding power consumption •u Correlating power usage to your physical infrastructure
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Selecting Your Teams Given the pattern of discovery laid out in Lewis-and-Clarking, you’ll need the right people for the jobs at hand. This section looks at creating the discovery portion of your project. For that you need one or two leaders who are capable of creating a pilot plan and executing it. Typically, these are experienced system engineers who are knowledgeable in IT and network programming. You need the people who can code up solutions, run data through spreadsheets, wire up new equipment, and, in general, overcome any obstacle to get through the plan. They are the type of people who can work with any kind of technology. They’ll have to work with facilities, telephony, networking, IT, and your business people. The type of people you are looking for are the ones who will get the job done. They will not be deterred by working with new technologies and will be able to work within your processes. After all, the pilot is about getting something up and running and collecting data. So the things to look for when selecting your pilot teams are as follows: Leaders Who Are Smart and Can Get Things Done This seems to go without saying. You can find people who are very smart, but when it comes to getting a job done, sometimes they just don’t produce. You need people who, when they see a boulder, climb over or dig under it if necessary. Not every tool or software system will be available to your team, so you might have to create scripts or programs as needed. You need people who can create on the fly and use what they’ve produced immediately. You need raw brainpower in your leaders. Look for people who are smart and have proven they can get things done. Leaders Who Can Build a Team and Get People Excited about a Project With any endeavor, you’re going to hit obstacles. When you select your leaders, they will have to add their own team members. They’ll also have to go to existing parts of your organization and ask people for help and time. They may even have to solicit help from professional networks and groups. That takes “people skills,” and if your leaders are not excited about the project and can’t convey that excitement, then it will be hard to gain support. You need leaders whom people will want to follow. Look for leaders who can get others excited about the project. Leaders Who Are Technically Proficient in Many Areas The interesting thing about energy management is that it touches so many technical areas. There will be databases to be mined, network equipment to check and configure, facilities equipment to be wired and to gather data from, programs to write, phones to check, software to install, and sensors and meters to rig. It takes smart people with the geek gene turned fully on to be able to work in so many areas. If they don’t already know how something works, you should have full confidence that the people you selected will be able to learn it and master it. Look for people who can do it all when it comes to technology. Leaders Who Are Thorough at Gathering Information You have to remember that the goal of the pilot is to get a system up and running and, during that process, to gather information. You need people who can keep that goal in mind and draw together the information. The information to gather is neither typical nor very scientific. It’s survey information on how your business operates, facilities contracts, systems and equipment you already have installed, and which parts are critical or noncritical to your business. You’ll need someone who can gather that information and then, when the pilot is over, make clear decisions based on what was learned. Look for methodical people who take good notes.
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| Chapter 5 Building a Pilot Deployment Leaders Who Are Adaptable The very nature of the pilot is to learn the things you didn’t know you needed to know. Not only is that a bit of a tongue twister, it is a tough principle to master. People are very comfortable when they know their surroundings and have a plan. For any pilot, you need people who are more comfortable in new areas—people who don’t fear change. In fact, you need leaders who thrive on it. You need people who can adapt and change their plans, but keep them within the scope of the ultimate goal. Look for focused people who can adapt. To summarize, the best approach is to select these explorers and then give them the ability and freedom to adapt and modify how they run their pilot. The thing that never changes is the goal of the pilot. It’s also critical that you give your leaders the freedom to adapt and change as new information arises. The benefit of doing a pilot with a few leaders who are adaptable and skilled is that you can change approaches and, if needed, invent new ones. You’ll benefit from past explorations detailed here, and there are some good examples from which you can learn. You may have to customize them to fit your organization, or you may find that you need something new. The pilot will help you judge the effort and find out where you might need more or less work. With the existing technologies, you should be well equipped to select, use, and/or customize existing systems in your enterprise to achieve your pilot. Choose your pilot leaders, and in the next sections you will learn about the goals, data, and particulars of their pilot.
How We Did It: Our Exploration Team Tirth Ghose and I (John) performed quite a few pilot explorations for different projects. We tried out various technologies when trying to come up with an energy management system. We were fortunate enough to be able to work quickly and autonomously to try out some approaches and explore what was available. We tried using servers and databases for energy network management based on existing technologies. After we tried a small pilot, we realized that we could not scale up in terms of size and speed. We realized that energy management that was built into the network would be far better than energy management that used the network. By trying out a few technologies and approaches, we asked the question, What do we wish we could do to make this easier? When we answered the question with our ideal system, we laughed initially with a sarcastic, “Yeah right.” After some thought, we realized, “Hey we can do that.” So when no technology existed, we had to invent a new one. The technology we invented eventually became Cisco EnergyWise.
Defining the Mission and Philosophy In wisdom gathered over time I have found that every experience is a form of exploration. Ansel Adams
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In order to have a successful pilot, you’ll need to define a simple statement of what the pilot should explore and how. The mission defines what you want to accomplish. Next you define your philosophy. This states how you want to accomplish the mission. For example, an exploration mission might be Become the first to reach the magnetic South Pole. The philosophy for that mission could be Use skis and sleds. Just as in physical land exploration, the philosophy you choose for your pilot data will shape the project. You can have plans with the same mission, but the technology you select will influence the execution. The explorers who first attempted to go to the South Pole all had the same goal—get to the magnetic South Pole. Some attempts used dog sleds while others used horses. Some expeditions even tried it with the first automobiles. The philosophy that worked was skis and sleds. The point is that those pilots all had the same goal, but the execution of each was very different based on the philosophy (including their technology selection). Driving a truck through ice and snow is very different from skiing. The same holds true for your pilot and energy management plan. For example, a first pilot program mission could be as follows Create a sample energy management system for our organization from which we can learn. This could be the philosophy: Use a simple database system for common data and reuse existing systems for specific functions. The first pilot has to be simple enough to accomplish, but, like any exploration, it has to result in finding out the details you don’t already know. As discussed in Chapter 3, “Assessing Value,” the best way to roll out a full energy management system is to use the existing systems you already have. The less you have to build, the more likely will be the adoption in your enterprise. What you need is a small system that could act as a launching site for all the other systems. It’s like building a portal website that launches into other websites.
How We Did It: Building a Portal In our pilot, we built a small database application that collects the common data from other systems and then adds historical power usage. We presented that data by creating some simple reports and user interfaces. From this system, we could launch into other established systems that had more fined-grained capabilities.
Facilities, IT, and telephony departments all have very specific and well-established systems for control and maintenance. There are dozens of systems available to an enterprise. There are fault systems from vendors such as Cisco, IBM, and HP. Other systems, such as those from SolarWinds, have applications that deal with all aspects of the management areas. You don’t want to duplicate the functions of those systems. You also don’t want to collect all the data from those systems into some master system, just the common data. What you want is to define a small set of identifying and classifying information that is common to all the systems. You report the basic power usage and identifying information. Then, if
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| Chapter 5 Building a Pilot Deployment someone wants to look deeper into an item, you provide the ability to launch into the specific system. If you can’t launch the other system because of technical limitations, you can provide instructions on how to go about accessing the system or whom to contact. For example, say your pilot system is showing a list of PCs that are being used as kiosks in public areas on a particular floor of a building. You look at a specific one and see that the PC is consuming power in the middle of the night when the building is closed. Thus you may want to adjust the time-of-day controls on that kiosk. The pilot system would provide the ability (or simply instructions on how) to launch into a configuration system for that specific kiosk. You don’t want to build or duplicate configuration management into your system if it already exists. Now that you’ve defined your mission and philosophy, the next step is to create a root system that has the basic information common to other systems. In that root system, you store the basic identifiers, and you add to that the history of power usage. Then you can run energy management reports from the root system and link to others as needed.
Creating the Root System The root system you create during the pilot should be the main area of focus for your initial Lewis and Clark engineers. They should create a system with database and reporting capabilities that can track energy information. The convergence of facility systems to IP-based ones can also give you an opportunity to get the information you need from the IP network itself. That’s the ideal. In reality, there will be many legacy systems that are not economically viable to update and deploy around the world. Some of them may be in your enterprise. So, at a minimum, you’ll have to set up some hardware and a database as an application server. You’ll have to define the data and then set up collection methods for the data. Then you’ll have to report what you find. One of the best ways to gain speed is to use systems that are premade and ready to use. If you can use preinstalled systems and sample reports, it’s easier to make changes to these systems than it is to build them from scratch.
Tip Keep in mind that the technology and database you select for the pilot do not have to be the same as those you will use for your production energy management rollout. In fact, that’s the best way to approach it—pick the technology that works best for the current situation and then be willing to adapt it later. Your root system should be deployed on some small pilot area first. Try to choose something on the order of a floor of a building, a small data center closet or rack, or perhaps even a lab. The rest of this section covers the hardware, database, and data you need to collect and store from your pilot area. Defining the specific pilot area is covered in the “Understanding Energy Domains” section that follows.
Determining Hardware Requirements You need at least one server to host the pilot. The cost for setting up a Linux or Windows server is very low. Some enterprises even incorporate these types of setups as virtual servers
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or cloud-based services. You should allow your pilot leaders to choose the systems with which they are familiar. If your team is experienced in Linux systems, let them install these types of physical systems. The same goes for Windows-based teams. The key here is to get a system allocated to the team and allow them to set it up as they see fit.
Tip With most pilot systems, a lot of changes are made along the way as you learn the things you didn’t know you needed to know. We’ve found that setting up virtual machines with products such as VMware or VirtualBox is an easy way to manage backups and frequent changes. Before we applied any changes, we simply backed up the machine. Restoring was very easy in case of mistakes. By using this approach, we didn’t waste a lot of time with system maintenance.
Setting Up the Database The next step for your veteran engineers in creating the pilot should be to install an application server for storing the root system and a way to report from it. This means setting up a web server, database, and reporting system. This is a fairly well-known and straightforward task. You might have existing servers and reporting databases that you can use as well. Your team should either reuse or setup services or software such as: Database This could be any relational database such as MySQL or perhaps space on an Oracle or IBM database your enterprise has set up. Nonrelational databases can be used as well. Web Server Web servers are straightforward to set up these days. For Linux setups, Apache servers are available with nearly every installation. Similarly, for Windows-based systems, the IIS server is available. Reporting Tools You’ll need some means of representing the data via pilot reports. This tool could be native to the database management system (DBMS) or a more graphical one such as Flash-based reporting tools. For the pilot, the key to selecting the reporting tool is to ensure that it is easy to connect to your database. You also want to make sure that the team is concentrating on making the content, not setting up the reporting environment. You’ll want a basic reporting tool that can be set up quickly. For example, by using Adobe’s Flex system, you can get reporting tools and dashboards that are premade and simply need to be connected to your database. Figure 5.1 shows some sample reports that can be easily customized.
Tip Use systems that have sample reporting, dashboards, and cookbooks. Then modify them as needed for the pilot. One way of saving time is to use prepackaged virtual appliances. If you are using VMware or VirtualBox as your base machine, you can download and install prepackaged appliances that contain web servers and databases already installed. These appliances save time in setup and enable your team to focus on the energy management application without wasting a lot of time setting up systems. Table 5.1 lists some popular virtual appliance resources.
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| Chapter 5 Building a Pilot Deployment Figure 5.1 Sample reports using Adobe Flex
Table 5.1:
Popular virtual appliance resources
Vendor
Link
VMware
www.vmware.com/appliances
VirtualBox
www.virtualboximages.com
Xen
www.xen.org
LAMP By far one of the easiest ways to get an application server is to use a LAMP system. LAMP is an acronym for a system installed with Linux, Apache, MySQL, and PHP. By using a prepackaged virtual appliance that has LAMP, your pilot team can get everything they need in one download. Configuring these appliances will depend on the specific LAMP appliance you download. The Distributed Management Task Force (DMTF) has defined an open format for defining appliances called Open Virtualization Format (OVF). Specifically, they have defined LAMP-based appliances and give you the basic configuration needed. Table 5.2 shows the DMTF open parameters for setting up a LAMP appliance.
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Table 5.2:
LAMP appliance configuration for DMTF OVF
Product
Property
Description
Linux
hostname
Network identity of the application
Ip subnet gateway netCoreRmemMax
Parameters to optimize the transfer rate of the IP stack
netCoreWmemMex Apache
httpPort
Port numbers for web server
httpsPort startThreads
Parameters to optimize the performance of the web server
minSpareThreads macSpareThreads maxClients MySQL
queryCacheSize
Parameters to optimize the performance of the database
maxConnections waitTimeout PHP
sessionTimeout
Parameters to customize the behavior of the PHP engine
concurrentSessions memoryLimit
The table lists 18 values for setting up an appliance. What that means for your pilot team is that they can get a complete solution by using a LAMP appliance and then configuring just a handful of parameters. Short of using an existing system in your enterprise, this is by far the simplest way of setting up an application server.
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| Chapter 5 Building a Pilot Deployment Emerging Technologies The convergence of facility systems to IP-based systems can give you an opportunity to get all the information you need from the IP network itself. That’s the ideal. In reality, many legacy systems will not be economically viable to update and deploy. Some of them could be in your enterprise. The goal of your pilot engineers is to inventory the devices in a small subset of your enterprise and begin to normalize and track the information. They could try new technologies to get data directly from the network.
How We Did It: Network-Based Data With Cisco EnergyWise, we turned the network into a distributed database in which we could collect the instantaneous power usage of connected devices. That gave us one place to go for information. Our pilot database then held the history and trending information of the network for reporting only. If you deploy Cisco EnergyWise, you can use your own network as a database for your pilot system. If your team wants to use a test network for their pilot, they can refer to the Cisco EnergyWise IOS Deployment Guide for information on how to set up a network-based database for energy management.
Choosing the Data If you can’t measure it, you cannot improve it. The more you understand what is wrong with a figure, the more valuable that figure becomes. Lord Kelvin If there’s any one phrase in facilities and telephony management that can be used as the golden rule, it’s “If you can’t measure it, you can’t manage it.” That quote, probably derived from those of Lord Kelvin, has become part of the collective meme among the members of facilities, telephony, and network departments. Software developers recognize this as a way to create systems as well. The results of simply measuring something can be very effective. The Pacific Northwest National Laboratory (PNNL) conducted studies showing that if consumers could measure their energy usage and monitor the prices, they would control their consumption with an overall savings in usage and dollars. The New York Times published an article in early 2008 highlighting the PNNL’s study (www. nytimes.com/2008/01/10/technology/10energy.html). The study and resulting article show that the simple act of measuring and monitoring results in people wanting to reduce their consumption. What this means for managers is that you don’t have to wait until you have fully automatic systems in place to realize savings. All you need to do is to start measuring and monitoring, and
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you can motivate your constituents to save. You need to set up measurements and a clear way to communicate the data. The data you choose will eventually be used as part of a feedback mechanism for automated solutions, but for now just defining the data is the most important step. When you first look at the problem, it might seem simple—just get the power usage for all the devices being managed and then report it. The problem is that not all devices can be measured, and the ones that are measurable don’t always report the usage with great accuracy or veracity. Typically, you look at the problem from the point of view of the device. Ultimately, some device should tell you how much power it is using. Over the course of 20 years of network management, I’ve had to deal with various systems and devices reporting data. The one thing you begin to realize when you have thousands of devices of differing types and brands is that a healthy mistrust is needed. It’s not that you expect lies when data is reported; it’s that people interpret requirements differently. For example, if I were teaching a course with 50 students and, as an exercise, I asked my students to report how much water they drank in a week, what kind of data could I expect? This seems simple on the surface, but I am sure to get some numbers in liters and others in gallons. How accurate is the data? How did each student come up with the answer? Some students might have changed their daily habits and used a measuring cup. Some might have estimated based on the amount of other liquids they consumed such as soda, beer, and soup. Some might have estimated based on samples and extrapolated. Still others may have not measured at all but just provided a number based on a thought exercise. Now imagine you wanted to measure that consumption not just for a single week but forever. Could I really expect every student to use a standard measuring cup forever? Their primary purpose is to be students, not water measurers. Device manufacturers are just like students with an assignment. The simple problem of measuring becomes a complex matter of making sure the data is uniform and then determining the value to the collector. Before you accept any data, you must require that it contain certain descriptive values along with the measurement: •u It must show the units used to measure it. •u It must include a description of how it was measured. •u It must come with some notion on the accuracy of the measurements taken. •u It must not be cumbersome, for the following reasons: •u You’ll have to do this forever. •u You don’t want to interfere with the device’s true purpose.
Keeping this in mind, when you set up your root system, you should select data that can track power usage in your enterprise and also describe how it was measured. The list of data can be expressed as a database schema in your pilot database. A database schema lists and defines the data elements that you will store in your system.
Note The data types that follow are divided into classes. The classes and data elements are derived from the pilots and experience from developing Cisco EnergyWise. These classes represent a condensed set of the data taken from examining power-consuming and power-producing devices, network management systems, facility systems, and telephony systems during our development.
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| Chapter 5 Building a Pilot Deployment The data is divided into three sets, as shown in the following list. The first is a definition of power-state levels that apply to all devices. The levels are a way of defining a common lexicon for describing the operational state of a device. The next two sets are classes that describe a particular device and its power usage. Power-State Levels This describes a common set of operational or nonoperational states to which a device can be set. Entity Class This describes a device or entity in your enterprise and contains fields to search and categorize the entity. The data here can provide business context for the entity. Usage Class This describes the power usage of the entity at a specific time. You’ll want at least one table of data to track the entities that can draw or meter power. Then you’ll need a historical data set of usage readings. Figure 5.2 shows sample data values from both the Entity Class and Usage Class and how they relate to each other.
Figure 5.2 Sample Entity and Usage data
Entity Data Class ID
Domain
Name
Category
Role
Importance
17 51 153
com.example.lincoln.building2.zone2 com.example.lincoln.building2.zone3 com.example.lincoln.building2.zone3
switch17 light51 light153
consumer consumer consumer
Public Access Hospitality Service Common Service
50 25 75
sales, public Cisco sales Schneider public Schneider
Lobby Phone Public Kiosk
100 50
emergency sales, public
459 com.example.lincoln.building2.zone2 1377 com.example.lincoln.building2.zone5
phone459 consumer pc1377 consumer
Keywords
Vendor
Cisco IBM
Source
Reference
2.2.2.2 2.2.2.3 2.2.2.3
energywise bms bms
2.2.2.2/17 energywise 2.2.2.5 energywise
Usage Class ID 17
Time 7/17/10 1:00PM
Level 10
Usage 60
Scale watts
Accuracy 1010
Caliber Actual
17 51 51
7/17/10 2:23PM 7/17/10 1:00PM 7/17/10 2:23PM
10 10 5
57 700 300
watts watts watts
1010 1010 1010
Actual Actual Actual
153 459 1377 1377
7/17/10 1:00PM 7/17/10 1:00PM 7/17/10 1:00PM 7/17/10 2:23PM
10 8 10 8
500 6 65 42
watts watts watts watts
0 0 0 0
Predicted Trusted Presumed Presumed
1377
7/17/10 4:01PM
0
0
watts
0
Presumed
Power-State Levels Every device manufacturer has a different notion of power levels. There are standards within the various communities of manufacturers, but there is no universal set of levels. The notions of On, Off, or Standby are fairly obvious, but you need a common language for expressing powerconsumption levels to prevent ambiguity. Cisco EnergyWise uses a set of 12 levels that can encompass the standards and levels from most, if not all, manufacturers. These 12 levels can be seen as an abstract interface that devices can implement to provide uniformity of management. The levels along with their names are defined so that there is a precise and universal meaning when speaking of a power state. Even though these levels are not yet a standard, you can use them as a way to normalize your device states. The levels are described in Table 5.3 along with their mappings to applicable standards.
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Table 5.3:
Cisco EnergyWise power-state levels
Level
ACPI Global/System State
Name
0
G3, S5
Mechanical Off
1
G2, S5
Soft Off
2
G1, S4
Hibernate
3
G2, S3
Sleep
4
G2, S2
Standby
5
G2, S1
Ready
6
G0, S0, P5
Low
7
G0, S0, P4
Frugal
8
G0, S0, P3
Medium
9
G0, S0, P2
Reduced
10
G0, S0, P1
High
11
G0, S0, P0
Full
Nonoperational states
Operational states
Entity Data Class The Entity Class data is used to classify and describe the smallest unit of power management. A member or entry in this class is any device that can draw or provide power in our energy domain. It can be a switch, a particular interface on a switch, a PC server, a lighting bank, or an entire HVAC system. You’ll want to allocate a unique identifier for each entity and then track usage for the entity. Figure 5.2 shows examples of the Entity Class data and how it relates to Usage Class data.
Note Remember, you are looking for basic data common to all systems for the pilot and root system. You could go into very detailed data modeling for power devices, but what you want is the simplest root data. The Entity Class has the following data attributes that you can add to your database schema:
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| Chapter 5 Building a Pilot Deployment Entity ID (Identifier) This is a unique ID used to describe the entity. You should generate a unique ID in your pilot system because not every entity you track will have an ID available. The ID should be unique across all of the domains and systems you are managing. Entity Energy Domain (String) This field indicates to which energy domain an entity belongs. This can be a floor of a building, a lab, or an entire campus. For simplicity, your entities should belong to only one energy domain. (See identifying energy domains.) Dividing an organization up into DNS-like names is helpful. So, for example, you could divide your organization with names such as com.example.newyork.building3.floor2.zone5. You can assign that domain name to all the devices in zone 5 of your New York office building number 3. We go into that in detail in the section “Understanding Energy Domains” in this chapter. Entity Name (String) This is a name for the entity that can also be used to identify the entity. For IP devices, this could be set to the MAC address. It could be set to the BACnet object identifier for non-IP-based entities. For comparison, the Entity ID ensures a unique identifier in your system. The Entity Name is typically a unique value from the system where you obtained the information. Entity Category (0-3) This field describes whether the entity is considered a consumer, producer, or meter of power. You need a way to distinguish the type of entity you are dealing with so that when you receive usage values, you can account for the usage and check to see whether the entity is behaving as expected. The values range from 0 to 3 and can be extended as needed. They currently represent the following: Meter This indicates that the entity is a meter that reads only the power consumed or produced. Consumer This indicates that the entity consumes power. Producer This indicates that the entity generates power. Hybrid This indicates that the entity can consume and produce power. Entity Role (String) This field indicates the entity’s purpose with respect to your enterprise. Typically, this field indicates the type of the entity such as Phone or PC or HP-SERVERMODEL—XX, but you can be more descriptive—for example, LobbyPhone or TellerPC. Entity Importance (1-100) In this field, you can rate the importance of this entity to your organization. Not every device is equal. A phone being used for mission-critical employees is more important than a phone used by interns. Phones for 911 calls might be even more important. If you are going to have effective power management, you’ll need a way to determine which devices are more important at a given time. You can set the ranges to any values you like, but the following is a suggested guideline: •u 90–100: Emergency-response devices •u 80–90: Executive or business-critical devices •u 70–79: Average devices •u 60–69: Staff or support devices •u 40–59: Public or guest devices •u 0–39: Decorative or hospitality devices
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Entity Keywords (List of Strings) This field contains multiple keywords that you can use to tag your entity. Creating a grouping scheme or categorization scheme becomes too cumbersome. What works for large sets of data is the ability to tag data and then later search for it. This field is a list of arbitrary tags that you can associate with the entity. For example, you can tag the entity as {public, private} to indicate how it is used in your organization. You can also tag it with the department to which it is assigned {HR, Accounting, Sales, or Engineering}. You could tag it with a value such as the user ID it is assigned or its location. The idea is to let this be a free-flowing value for which you can search. The power of tagging and search makes the data easy to store and then summarize. Entity Usage Vector (Array 0-11 of Power) This field is an array that maps to the powerstate levels. Each value in the array represents the maximum power the entity would draw if it were set to a specific level. This array of values will enable a device to advertise its power characterization. Devices may have more or even fewer physical power levels. This array indicates a mapping from the device’s physical capabilities to the levels outlined in Table 5.3. For example, an array of {0,0,5,5,10,10,20,20,20,20,100,100} would indicate that at the maximum level 11, the entity would consume 100 watts while at level 4 it would consume 5 watts. An array of {0,0,10,10,10,10,10,10,10,10,10} would indicate that the device essentially has only an off and on power profile. These values can be used for what-if and planning reports. Entity Usage Vector Scale (–24-24) This field is an integer value that represents the scale factor associated with the entries in Entity Usage Vector used. The value is based on the International System of Units (SI) notation and ranges from –24 to +24 indicating 10 –24 through 1024. For example, a value of –3 would indicate that each value in the array was considered a milliwatt reading. Entity Vendor (String) This field is a string indicating the vendor or manufacturer of the entity. Entity Source (String) This is a string indicating how to access the entity. It might be the host name, IP address and port, or simply a description of how to access this entity for data— for example, 10.10.10.2:80. Entity Reference (String) This is a string that contains a reference on how to access the entity at the Entity Source. It could be a URL string or some reference that is specific to the entity—for example, a path such as /location/file.
Entity Power Usage Class This set of data is used to describe a power usage reading at a specific time for an entity. Each entry or member of this class is a power reading for a specific time for a given entity. The Usage Class data describes usage over time for a device. As shown in Figure 5.2, Usage Class data are readings at specific times for a device that relates to the Entity Class data. The usage data represents the history of usage for the entity data and should contain the following attributes: Usage Time (Timestamp) This field is a timestamp representing the date and time at which a reading was taken.
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| Chapter 5 Building a Pilot Deployment Usage Level (0-11) This field indicates the power level to which a device was set at the usage time. Usage (Signed Integer) This is a value expressed in watts that represents the power usage at the usage time. A negative number indicates that the power was supplied, and a positive number indicates that the power was drawn. Usage Scale (–24-24) This is an integer value that represents the scale factor associated with the units used to measure the power or energy. The value is based on the SI notation and ranges from –24 to +24 indicating 10 –24 through 1024. For example, a value of –3 would indicate that the usage represented a milliwatt reading. Usage Accuracy (0-10,000) This field indicates a percentage value in 100ths of a percent representing the precision to which the Usage field was measured. For example, 1010 means the Usage field is accurate to +/– 10.1 percent. This value is zero if the accuracy is unknown. Usage Caliber (0-4) This field specifies how the usage value reported by Usage was obtained. It qualifies the usage value by giving an indication of the confidence by which it was obtained. Five values are listed here, but for your pilot you can add more to categorize the types of readings available from your systems: Unknown This indicates that the way usage is determined is unknown. In some cases, entities report aggregate power such as what a lighting controller or aggregate controller does. In such cases, it is not known whether the usage reported is actual or presumed. Actual This indicates that the usage data reported is not presumed or predicted, but represents the real power drawn. A PoE phone drawing x amount of power can be determined by reading from the port. For example, a PoE phone can report the actual usage as x W. Trusted This indicates that the usage data reported came from another source. Trusted is higher caliber than Predicted. Predicted This indicates that the actual power drawn cannot be determined. The value is an estimate based on the device type, state, and/or utilization. Predicted is higher caliber than Presumed. For example, a switch is known to draw 200 W when PoE on all interfaces is disabled and 600 W when PoE is fully enabled. Presumed This indicates that the actual power drawn cannot be determined but can be presumed from the model. Presumed is the lowest caliber. For example, a PC Model X draws 200 W, while a PC Model Y draws 210 W.
Note The data we describe here is a subset of the information we used to create a complete system. The full set of data was applied, defined, and became the data model for Cisco EnergyWise. The data model for Cisco EnergyWise is not much larger than this set of data, yet through searching keywords and tracking the usage, we were able to build very powerful data models and control systems. Entire energy management systems from Cisco, IBM Tivoli, SolarWinds, and other Cisco partners are developed using this base information. We can create sophisticated what-if scenarios and respond to peak demands by using this simplified data. Given the data model just defined, you can come up with some very complex and powerful operations. For example, if you wanted to know the current power usage of all the hospitably
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rated devices in a specific power domain, you could query the domain fields and the importance fields. If you wanted to know the power usage of all devices tagged with kitchen for a specific period, you could query entity data and reference the usage values for that specific period.
Gathering the Data For the pilot system, the mission is to create a sample energy management system for our organization from which we can learn. This task will help you achieve a working system, and it will also help you in the second part of your mission—that is, to learn from the exercise. Gathering the data described in the previous section is where the rubber meets the road. This is the task that will occupy the time and skills of your pilot engineers. The previous section described the classes and data elements that you want to collect. It’s a small set of data and, once collected, it can provide you with powerful reports and control. Even though the data you want to collect is small, gathering it from different systems, directly from devices, and over varying protocols is a logistical challenge. Table 5.4 provides a list of facility device protocols.
Table 5.4:
Some BMS protocols
Protocol
Description
BACnet
Building Automation and Control Networks
LonWorks
ANSI/CEA-709.1-B from Echelon Corporation
Modbus
Serial communication from Modicon
C-Bus
Clipsal Bus closed protocol
SNMP
Simple Network Management Protocol
oBIX
Open Building Information Exchange
Facility device and power distribution unit (PDU) vendors from companies such as Johnson Controls, Schneider Electric, Honeywell International, Raritan, Western Telematic Inc, (WTI), and Cyber Switching have hundreds of products among them with varying protocols and data capabilities. Additionally, Building Managements Systems (BMSs) and Network Management Systems (NMSs) like those from Johnson Controls Metasys and IBM Tivoli will have information aggregated in their internal data stores. Your pilot system will also have to gather data from PC management systems so that a complete view of energy usage can be reported. The major task for your pilot team will be to inventory the pilot area to find out what vendors, product lines, protocols, and systems you have deployed. This section will list examples of these systems and show you how to map the data in those systems to your pilot data. Ultimately, your pilot team will have to inventory and research how to collect the data from the specific systems. Though this section is a guide on how to start that task, it cannot list all the possibilities given the sheer number of device types and product lines that are involved. Chapter 6 describes how you can use audit data to help you in further expanding the information you are gathering in the pilot.
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| Chapter 5 Building a Pilot Deployment There are systems that collect and provide energy management. Cisco EnergyWise is an attempt to provide a central source for all this disparate data. Systems such as IBM Tivoli and SolarWinds Orion provide a platform for collecting management data as well. They have added energy management data, and these systems can be used directly to manage IT power consumption. However, it will take time for the facilities, data center, IT, and telephony systems to converge. Until then, for the pilot and the systems you need to create, you’ll need to manually gather data from other systems that will eventually converge to IP based management over time. Your pilot engineers should be ready to invent and provide features to gather data into the root system. To start, create a spreadsheet of data that you populate manually. You can do this by running reports from the NMS and BMS systems you are deploying and saving the data exports in CSV or TXT format. As you physically take inventory of your pilot domain, you can list the vendor and/or access method for the devices in the domain. When you’re finished, you can then list the programs and scripts you might need to write to populate your pilot database.
Note You might have organizational processes and workflows defined to manage shutdowns and maintenance windows. The root systems should coexist with these processes and/or systems. The root system should supplement your existing systems and not try to replace them. The data collection should not interfere with existing systems. The following list describes the types of systems from which you can extract data along with specific examples from commercially available systems for each type: Cisco EnergyWise If your IT administrators have deployed the new Cisco EnergyWise features, the data you need can be obtained via SNMP from each device or by using the broadcast query capabilities of Cisco EnergyWise. Eventually, as all devices converge to IP-based, the Cisco EnergyWise query capabilities can be used to collect all the data you need from the different systems. The custom code that you write for each type of system to populate your data will converge into one convenient method. Cisco EnergyWise has an application programming interface (API) available to Cisco partners to issue network-based queries to a domain of switches. The API provides a method similar to an Open Database Connectivity (ODBC) library for database queries. To see a sample of how the queries would work from your switch command-line interface (CLI), you can run the following commands: SwitchA# energywise query importance 60 keywords HumanResources collect usage EnergyWise query, timeout is 3 seconds: Host Name ------192.168.1.10 phone1 192.168.3.10 phone2 Queried: 2 Responded: 2
Usage -----
Level Imp ----- --3.71 (W) 10 3.71 (W) 10 Time: 1.4 seconds
60 60
Building Management Systems (BMS) Your facilities systems will inevitably contain products from vendors such as GE, Schneider Electric, Johnson Controls, and Delta Controls. These systems and devices will provide you with metering data as well as data from lighting, HVAC, and other facility devices.
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Johnson Controls’ Metasys BMS application contains a GUI-based extract system that allows you to export data from the system. You can specify the data elements needed and the resulting formats. The system can export directly to Microsoft Excel or to formats such as CSV, TXT, tab-delimited, XML, HTML, and Microsoft Access. For your pilot deployment, you can initially set up an export via the GUI for testing and then schedule backups for recurring data. PC Systems PC power management applications such as Verdiem Surveyor, Cisco EnergyWise Orchestrator, or 1E NightWatchman provide visibility into PC power data. Most PC power applications use a Microsoft SQL server as their data store. Typically, you are asked to administer the database. In those cases, you can use the SQL query capabilities to extract the data. You will have to ask your vendor for the database schema or you can browse the database and determine the schema directly yourself. Data export utilities are not very common in these systems because the products are just maturing. A wide variety of prepackaged reports are available. 1E offers the NightWatchman product for PC power management. They offer an add-on called the NightWatchman Agility Framework Product Pack. With this optional pack, a variety of reports can be obtained via their web-based reports. By programmatically opening the URL http://YourAgilityServe/AFConsole, you can obtain data needed for the pilot. Network Management Systems Systems such as IBM Tivoli, CiscoWorks LAN Management Solution (LMS), HP Business Technology Optimization Software (formerly HP OpenView), SolarWinds Orion, and HP ProCurve all provide visibility into the power management of network devices. The NMS system space is very mature, and data exports are typically the bread and butter of the system. Both Cisco LMS and IBM Tivoli have extensive job control and export facilities for extracting data that you can configure from their network administration consoles. IBM Tivoli has by far the widest range of methods for data extraction. They support scheduled exports as well as native SQL interaction with their data store. SolarWinds is another NMS system that has a convenient data extract. Their main product line is called Orion. Orion contains web-based reports, and by appending a format parameter to any report URL, you can get data suitable for programmatic storage. So appending &DataFormat=XLS to the URL will provide the data formatted in Excel. Additionally, any report via the UI can be saved in a CSV format. Smart PDUs Power distribution units from American Power Conversion (APC), WTI, and Cyber Switching provide APIs via HTTP or SNMP to collect metered data from the power units directly. Major PDU vendors implement some sort of SNMP agent for data extraction. So, for example, if you have a PDU from WTI, you can collect the usage for a specific plug via the following SNMP get from a UNIX host with network connectivity to your PDU: snmpget -v2c -c private -m WTI-MPC-MIB ipaddressOfPdu plugTable
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| Chapter 5 Building a Pilot Deployment Understanding Energy Domains There are no big problems; there are just a lot of little problems. Henry Ford So far, you have been examining how to execute your plan. First you gained support by creating the business case for your expedition and you secured funding. Next you defined your pilot mission and stated your philosophy for the journey by knowing what data you want to manage and monitor. You’re all packed and ready to go, but you haven’t determined yet where you are going or how far you will go. Before you start, you have to look at your business or enterprise and divide it into manageable sets. These sets will be the areas to which you are going to apply your technology and philosophy. For the pilot, it’s best to start small and practice on a small area. Then expand as you gain more confidence in the system you built.
Natural Domain Structure The term domain is probably one of the most overused in computer science. There are problem domains, security domains, Windows domains, and more. What it comes down to is grouping the things you want to manage into sets. Traditionally, this was a matter of creating hierarchies of types—the dreaded folder metaphor people endure in file systems. With the advent of search technologies, the rigid structures that were once applied to data hierarchies are giving way to flatter organizational schemes. With energy management, you’re going to need to track a lot of things. You want to be able to track the energy usage of everything that draws power. You’re going to want to know the energy usage for buildings, campuses, network equipment, banks of lights, and so forth. Ideally, you should be able to group things from the smallest light up to the largest campus. Trying to maintain a hierarchical structure with so many elements can be daunting. Whenever people are faced with organizing things from email, to photos, to web pages on the Internet, they typically turn to hierarchical structures first. That’s a relic of past technologies. These can be useful for small amounts of things, but as the number of items increases, the task becomes unmanageable. If you have tons of email, photos, or files to organize, you’re probably familiar with this conundrum. You might be tempted to create folders that resemble what you are searching for rather than the natural order. Now imagine you want to store information about everything that draws power. That’s a lot of things to track. One example of how a hierarchical system became unmanageable can be seen by looking at the evolution of the organization of information on the Internet. When Yahoo! first started to crawl and organize data on the Internet, they processed each item they found into categories. The cross-referencing became unmanageable and eventually not very useful. The game-changing paradigm shift came when Google used the natural structure of the Internet as the primary way to organize the data. They divorced this from the physical way to store and retrieve the data. If you could look it up and group it fast enough, you could simply store the data in one big set or lots of little unrelated sets. In fact, modern organization of data is favoring meta tags and free-form key-value structuring of data in very much the same way. This same paradigm shift can be used to find the natural order among elements in an energy domain. Meters express this natural order.
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The most natural energy domain maps exactly to what you can meter. Metering and billing are the natural order within organizations for energy data. Think about it as if it were a home and you can see the natural order—the domain would be the house that typically has one meter and one bill. Any other criteria are simply the things you wanted to search or summarize in order to break down what the meter was reporting. Ideally, you’d like to be able to list all the items discussed in the monitoring section in one database or subnetwork and just search and summarize the data. The same problem exists in network management. Keeping track of every switch and every data port in an enterprise would require a large database of information. If an enterprise is a worldwide corporation, the sheer numbers of things to manage and track grows very quickly. Think about the number of electrical outlets in your home, and then try to envision tracking every electrical outlet in a corporate building. Multiply that by the number of buildings and it quickly becomes a monumental data-tracking problem. At some point, you have to put a limit on the number of things you want to manage unless you want to build a massive data center to track it all. In network management, this is called a management domain. If you are familiar with SNMP management, this is equivalent to an SNMP community. It’s a way of saying, “These devices here are a logical unit of management.” Sometimes it’s a building, a location, or even a rack of equipment. The point is that it’s arbitrary. It’s an understandable limit on a vast set of information. You have to draw a circle around sets of things you want to manage and then manage sets of those sets. If you can do this and map them to a submeter or billable unit, you have a natural order using network management-like tools.
Smart Loads When a device or a set of devices are not just blindly consuming power but are aware of their power consumption, this is called a smart load. The power consumption of a smart load can be adjusted by outside management or automatically by the units themselves. Think of this in terms of your home thermostat. For decades, the only way to control the temperature of your home was via a thermostat whereby you set the temperature. With the advent of “smarter” thermostats, homeowners were able to program the temperature they wanted for any time of the day. So in the mornings, the heat would be set to 72°, perhaps down to 60° during the day when no one was home, increase to 72° again in the evenings, and drop to 62° again while everyone was snug in their beds. Thus heating and air conditioning became a smart-load point for the home. For the entire home to become a smart load on the public utility grid, you’d have to make not only the HVAC intelligent via the thermostat, but all the major appliances, pool heaters, and so forth—all controlled as a single unit. Once individual homes are controllable, they can be aggregated into manageable sets. This would be analogous to creating energy domains in your business. You want to report and control consumption in your business, such as individual lights and computers, at a fine-grained level. However, you want to aggregate them into a smart load or domain. To a utility, the finegrained items such as homes will eventually need the same aggregation into residential blocks or cooperatives with their own rating of context and importance. For example, a house versus a hospital would have different context ratings. That’s the task when making a smart load. It’s not enough to make portions of it intelligent. The idea of a smart load is to aggregate the usage of a building or some metered set of devices into an intelligent consumer of power and then give it context.
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| Chapter 5 Building a Pilot Deployment Cisco EnergyWise Domains Cisco EnergyWise uses the organizational structure of the Internet to group energy-consuming devices into energy domains. If everything that draws power is converging upon network connectivity, it makes sense to use that structure as the organization. Cisco EnergyWise recognizes two types of devices when creating domains: Endpoints These are the power consumers. They are typically Power over Ethernet (PoE) and non-PoE devices that connect to the network. These include nontraditional network devices such as facility controllers, lighting, HVAC, and so on. Domain Members These are the switches, routers, and network controllers that make up the data network. They are like endpoints in that they draw power, but they also have the ability to act together to propagate messages across the network to form a Cisco EnergyWise domain with other domain members and endpoints. The task of creating an energy domain that corresponds to a submeter is made easier with Cisco EnergyWise. The network devices are merely grouped into a domain and, as endpoints are connected or disconnected, they join or leave the domain. Type
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You need to be able to set controls for an entire building or campus and then have the parts respond to these controls. A smart load has the ability to do the following: •u Schedule the time it’s on or off •u Respond to its environment •u Respond to requests from the providing network •u Coordinate with other intelligent devices to achieve a goal or accept a restraint
When selecting your energy domains, it’s important to keep in mind that these sets of devices can be used as intelligent loads on the grid. When the loads can be adjusted, the grid itself can become intelligent.
How We Did It: Using Past Experiences When I [John] worked for the gas utility in New York City, I had to tour the company and see how the different areas operated as part of my management training. One of the highlights was to visit the operations center. Even in the late 1980s, this room looked like a futuristic war room. There were large boards showing the gas flow and projections showing the status of the major arteries of the city. Operators at various workstations looked like they were preparing for a moon landing. We had to arrive at 5 a.m. to watch as New York City residents started to wake up. The mid-80s was the time when homes with programmable thermostats were becoming the norm. As the hour progressed to 6 a.m. and then 7 a.m., you could see the stress rising on the network like a high tide. As the heating demand started to fire in unison around the city, the operators scrambled to maintain pressure and availability. An operator, in typical Brooklyn style, described this to me as the Big Suck. What I was seeing was a smart grid. It was made smart by the control room and operators. I saw smart loads —programmable thermostats — communicating with a smart grid. Brooklyn operators scrambling and adjusting the network made that grid smart. What I learned was that after you provide intelligence to the consumption points, the network will be stressed because of the change in paradigm. These transition periods are what you need to manage as the technology and your plans evolve.
Energy Domains as Smart Loads Managing energy comes down to creating a collection of devices and then doing the following: •u Monitoring the energy consumption •u Controlling the devices individually •u Controlling the devices as a group •u Reporting the usage
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| Chapter 5 Building a Pilot Deployment •u Verifying the usage versus some meter or bill •u Setting up rules or policies for the devices or group
A collection of devices is called an energy domain. This pattern of grouping devices and then having the devices controlled can be applied on a micro level in the home, a higher level for corporations, and a macro level for the entire utility grid. It comes down to enabling a device in the energy domain to be managed or to manage itself. Devices become smart when they can be managed or manage themselves. When a collection of devices has this capability, it can be called a smart load. Let’s look at some examples: Homes A home contains a collection of devices. You can track the energy usage via the home meter and get the billing from the utility. If every light, thermostat, or appliance has the ability to be controlled or programmed, you can call this home a smart load. Offices / Businesses A corporate office is a collection of devices. You can track the energy usage from a meter or set of submeters. You account for the billing typically from a facilities division or agency. If every light, thermostat, PC, network device, phone, printer, fax, breakroom appliance, unit of HVAC equipment, and so on could be controlled or programmed, you would call this corporate office a smart load. IT Networks An information network is a collection of PCs, servers, switches, routing equipment, and networked endpoints. If you could track the usage, correlate the usage to billing, and control or program the usage, you would call a network a smart load. Utility Grids An electrical network is a collection of power-consuming loads. If you could track the usage, correlate the billing, and control or program the loads, you could call the utility grid a smart grid. There’s a pattern here: Smart loads are endpoints or devices in a group that consume power and have the capability to be managed or to manage themselves. One thing that we noticed is that there is becoming less of a distinction between homes, offices, utility grids, and IT networks. Everything is becoming internetworked. We could look at these areas as distinct problems and create solutions for each. But what if we take advantage of the changes taking place and view each area as nothing more than an information network? In order to get smart loads, you could look at the problem as trying to create smart homes, smart offices, smart networks, and smart grids. If we realize that everything from the home to the utility grid is an IT network, and if we can transform a network into a smart load, then we can transform everything into smart load.
How We Did It: Cisco EnergyWise Creates Smart Loads Cisco EnergyWise is a capability that we built into the fabric of the Internet. Cisco switches and routers make up the bulk of the world’s internetworked devices. We realized that if we could add the capability of managing the power of anything attached to these devices, we could turn networks and subnetworks into smart loads. With every home, business, and utility becoming internetworked, we could then transform the world’s energy loads into smart loads.
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Selecting Pilot Energy Domains The most natural energy domain is some unit of measurable (metered) power. Meters and submeters can indicate our power draw, but breaking that down into smaller slices is difficult. The natural energy domain should be something that can be searched and summarized to provide that breakdown. Another benefit of having an energy domain that corresponds to a meterable unit is the ability to create smart loads.
Tip You should try to have fine-grained metered areas of power usage. When we were selecting domains at Cisco, we either mapped the domains to submeters or added submeters as needed. For offices, we found that we had metered floors within buildings. For labs and data centers, we found that adding hard or soft submeters to racks of devices allocated to different people helped in creating more-granular reporting. When you start your pilot, you’ll have to select a small area to manage at first. This could be a lab or simply one floor of a building. In the entity data class, the field Entity Energy Domain can be used to describe this manageable area. The field is intended to hold a name. A good way to select the name is to look first at the entire organization and start to divide it into sets. If you are familiar with the Internet Domain Name Service (DNS), you can use that type of partitioning to divide your organization. The DNS is a hierarchical naming system for computers or any resource connected to a network. Let’s take a sample company and divide it into domains. Let’s say our company is called Example. The company has four buildings in two cities: Lincoln and Washington. Each building has submeters, which divide the buildings into three zones each. By using a DNS-like naming scheme for the zones, we would come up with the following domains: •u com.example.lincoln.building1.zone1 •u com.example.lincoln.building1.zone2 •u com.example.lincoln.building1.zone3 •u com.example.lincoln.building2.zone1 •u com.example.lincoln.building2.zone2 •u com.example.lincoln.building2.zone3 •u com.example.washinton.building3.zone1 •u com.example.washinton.building3.zone2 •u com.example.washinton.building3.zone3 •u com.example.washinton.building4.zone1 •u com.example.washinton.building4.zone2 •u com.example.washinton.building4.zone3
Thus all the devices drawing power in zone 2 in Lincoln that you inventory and add to your database should have the attribute of domain set to com.example.lincoln.building2.zone2.
| Chapter 5 Building a Pilot Deployment Now say you have a small lab in building 4, zone 3. You could set up a submeter or smart PDU for the lab and use that as your first pilot domain. So you’d have com.example.washinton. building4.zone3.pilotlab . You could use that as the domain name for all the entries in your database that are powered in the pilot lab. Chapter 6 covers energy domains in more detail. Figure 6.1 in that chapter shows an example of a typical building using this DNS-like naming. After you’ve selected your pilot domain, you can begin to take meter readings for the domain and collect usage data, as described in the “Choosing the Data” section earlier in this chapter.
Communicating Results After you’ve set up your database and populated it with data from your pilot domain, you’ll have to report the data. Depending on the audience, there will seem to be an unending variety of ways to select and report the data. We’ll go into that in detail in Chapter 6 and Chapter 8, “Reporting.” What you want to do for the pilot is to get a good usage report that will show value and inspire people to save energy. As we discussed, after people see their power usage, they will try to reduce their energy consumption even if it is by manual means. So it’s important to get information presented and communicated as soon as possible. If you’ve selected your pilot energy domain and simply started collecting metered data, you’ll be able to report information similar to the usage curve in Figure 5.3. Daily Metered Usage
Figure 5.3 Metered power usage in a domain
50000 45000 40000 35000 30000 Usage
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This report and usage curve is nothing more than what you could have gotten from a BMS system and meter reading. You can see the peaks and valleys of your usage for the metered zone, but you have no visibility into what devices are causing this usage. It’s like graphing the energy usage of your home power meter. You might know that your power spikes at 4 p.m.,
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but you don’t know if it’s because your kids came home and turned on their computers or the lighting. The meter reading doesn’t give you enough information. Detailing the entities in the domain and collecting the usage (no matter how accurate) will give you visibility into what is causing the peaks and valleys of the curve. That’s the point of collecting the data in our system. We want to detail the area under the curve in Figure 5.3. You’re first and best report/graph is to give visibility into the meter readings. If you summarize the usage data of the entities by keywords or even report them individually, you’ll get the information you need to show value for your pilot. The graph in Figure 5.4 is much more useful because it shows the details of where energy is being consumed and gives you the information you need. The more values you chart and track, the higher the resolution of the area under the metered usage. Daily Usage Higher Resolution
Figure 54 Higher-resolution power usage in a domain
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The Bottom Line Select an engineering team for your pilot program. When you start on your pilot, you’ll have to select a small engineering team that can start the program and then grow it into a production rollout. Master It You’ll have to be able to identify the right people for the job. You’ll need to find a core team you can grow throughout your organization. How do you select the right team for a pilot deployment?
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| Chapter 5 Building a Pilot Deployment Identify a mission and philosophy for your pilot. You’ll need to define goals for your pilot and how you will achieve these goals. Master It You’ll have to define a mission and philosophy for your pilot and communicate it to your team. Select a pilot energy domain. When you begin to execute your pilot, you’ll have to select a small area of management. You should start by dividing your organization into power domains that roughly match metered zones. Master It Take your organization and divide it into manageable units of power consumption. Identify the basic data sets that you need for your pilot system. You’ll need a system to track the entities in an energy domain. You’ll also need to provide power usage readings for those entities. Master It You’ll need to be familiar with the data fields and formats and know how to collect and report the data. Inventory the systems you will deal with and find out how to access the data. The data you’ll gather for your pilot database will come from varying sources. You’ll need to be familiar with the various systems. Master It What are the systems and types of entities in your energy domain? How will you access the data?
Chapter 6
Pilot to Production You’ve finished your prototype. You have a system that can get data feeds and store them in a database. All you need to do is roll your energy management system out to production and you’re done—wrong. You’ll never truly be finished with your rollout. That’s not a hopeless statement. It’s something you have to embrace. It’s like dieting—you have to change your way of thinking and living and then practice it for life. The same thing applies to energy management. No system is ever rolled out and done. It evolves. When you are planning to roll out your energy management system, you have to embrace change and add the ability to accept enhancements to the system itself. Your prototype was a way of discovering what you didn’t know that you needed to know and also of getting data feeds. You had a small team assess the problem and try out a plan. Now you’re ready to get more people involved and start to change your enterprise. Bit by bit you’ll add energy management around the facilities and network infrastructure. When rolling out any new technology, a gradual wiring up takes place. At the turn of the century, when cities rolled out electricity, city planners and the emerging utility companies gradually electrified sections of cities at a time. The same thing followed for telephony. As the Internet first took hold, we gradually “wired up” campuses. A few years later, we lit up campuses by rolling out Wi-Fi in a similar manner. If you look at these technologies, you’ll see that none are really done. No campus is every really done. The same is true when making enterprises energy aware. You’ll have to enlighten sections at a time and be prepared to make enhancements as you go. In this chapter, you will learn the following: •u How to create an energy management production plan •u How to partition your enterprise into manageable rollout areas •u How to categorize and rank your inventory of power-consuming devices •u How to differentiate active, manual, and passive rollout activities
Creating a Production Plan Energy management is an area of your business that will span both your network and facilities administration. Thus, if you are going to be managing energy, you have many possibilities. Your facilities, network, and energy systems have to be managed. But what type of management should you use for these systems? Ask a facility expert and they’ll say use a BMS system for control and management. Just add network and data center information to your BMS. On the other hand, if you ask your IT and network administrator, they might say give them access to the facilities systems and they’ll manage energy all from their NMS. Neither opinion is wrong or right. Energy management can be done either in a BMS or NMS with some degree of success.
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How We Did It: Using a BMS or NMS? We discovered that using either a Building Management System (BMS) or a Network Management System (NMS) exclusively does a disservice to the emerging field of energy management. We found that having a separate portal system that links to facility BMS and IT NMS is a better approach. This is similar to the evolution of telephony from exclusively POTS (plain old telephone service) to Voice over Internet Protocol (VoIP). While the change is taking place, the old and new forms will overlap for a time, until the newer form becomes more dominant. We recognized this change in energy management as being similar to the telephony change, and we used our experience in VoIP rollouts to help us with the energy management transition. We embraced the fact that there are information silos, and we opted for a portal-based approach to unify the silos.
With facilities systems converging on IP, rolling out a separate system for energy management is an approach in line with how a converged system can be managed. That’s why we create a separate portal system in a pilot. As we get ready to roll out to production, it’s best to have a plan on how to roll out the separate system. At the highest level, your production rollout has the following steps: Prepare •u Review your pilot. •u Partition your campus into manageable areas.
Monitor the Rollout •u Select an area. •u Inventory and categorize. •u Add monitoring. •u Establish a baseline.
Implement Rollout Policies •u Select an area that is monitored. •u Implement manual policies.
or •u Implement active policies.
The next sections talk more about the steps, activities, and concepts for the rollout.
Tip Don’t worry about trying to have a complete system with automated policies and every bell and whistle hooked up. You’ll get there over time. If you rolled out a production system that only monitored energy across your enterprise, you’d achieve substantial gains. Monitoring leads to better visibility, which leads to savings.
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Reviewing the Pilot New York will be a great place—if they ever finish it. O. Henry In Chapter 5, “Building a Pilot Deployment,” we outlined the energy management data you should monitor and collect in a pilot implementation. We recommended setting up a database of the information with feeds and then providing initial reports that show the power consumption. The pilot enabled you to record the types of devices, locations, people, and processes that you may encounter in a rollout. You should check your pilot implementation and ensure that you have gathered the information outlined and that the system is acceptable for rollout. You’ll have specific data feeds.
Tip We developed Cisco EnergyWise as a way to get our energy management data from one source—the network. As time goes on, more data will be available from the network. In the interim, you may have to provide custom-developed data feeds or work with aggregation systems that provide this data. You spent a lot of time getting buy-in and approvals from your management. At the end of your pilot, it’s time to look at the system, review it with the stakeholders, and make sure that it is ready to roll out. You don’t have to wait for the system to be “done.” Recognize that the system will never be done, and establish what you think is good enough to begin your rollout. You should check to make sure that the system is ready to do the following: •u Store the data described in the pilot plan •u Collect and report the data •u Provide access authority and restrictions
Note Make sure the pilot system database is designed to accept new data based on energy domains.
Partitioning If you have a small business or home, adding an energy management system is fairly straightforward. You have to monitor the power consumption of the devices and meter the office or home. Because the area is small, you can roll out the system in one step. When you have a large building, enterprise, campus, or worldwide operation, you have to divide the job into manageable tasks. How you divide up the rollout is based on your point of view. If you are facilities or electrically minded, you’ll see a building as a distribution grid of power. This is a good view to keep in mind. We’ve talked about energy domains. These are the smallest units of energy management typically associated with a meter. The meters are typically placed at power distribution points. You could look at a building as a collection of energy domains, as shown in Figure 6.1. The domains equate roughly to the distribution panels in a typical electrical layout. However, when you roll out your systems, you have no idea about the business context of the domains and devices.
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| Chapter 6 Pilot to Production Figure 6.1
com.example.building
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com.example.building.elevator 8
com.example.building.chillerb com.example.building.panel-8-1ldp3
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com.example.building.panel-8-1ldp1 through com.example.building.panel-8-1ldp6
Rolling out an energy management plan is a social exercise. The logistics of the rollout have to map to social and human contexts. The data you store and how you monitor and manage the energy are technical problems, but the logistics are human. We found that looking at a building as a set of human activities and rolling out the system based on the human context encapsulates the business risks and maximizes your support. Which sounds better to your employees? •u Everyone using devices in building 8 on panel 5 will be converted. •u Everyone in Accounting will be converted.
Obviously, the second is simpler and sounds much better. By rolling out energy management and socializing the exercise, you’ll enlist the support of the residents of the building. First create your technical energy domains, but then group them by the social groups and teams in your business. Figure 6.2 shows an example of grouping your physical energy domains based on the business context.
Tip Foster a bit of healthy competition among the users. Throw down a challenge to your teams. Perhaps setup a website to track the biggest savers of power and announce the leader every month. Some giveaways or a celebration can help make the project fun and engaging. A fun internal competition will help you get support. After you’ve identified the rollout areas, you can select the order in which the areas will be converted. You should select an area and then begin to inventory the items in it. Then repeat this process for each subsequent area. This is exactly what you did for your pilot except that now you’re expanding it.
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Figure 6.2 A building overlaid with rollout areas by social context
Commons Data Center
Trading
Call Center
HR
Lobby
Performing Inventory and Categorization As in your pilot, you’ll have to inventory and collect data. When you first did the inventory for the pilot, you could do a physical inventory and pretty much gather the data by hand. For a rollout to production, you’ll want to import this data from existing systems. In network and facilities management, there’s a practice of auditing the systems. These audits have collections of the data you’ll need. You’ll want to get the data for one rollout area into your system and then begin the process of categorizing it. For example, in Figure 6.2 we identified the following areas we wanted to roll out: •u Commons •u Data center •u Trading •u Call center •u Human Resources (HR) •u Lobby
Let’s assume you want to roll out to the HR area first. You pick this area because it is a logical grouping of elements for one team—the Human Resources employees. You can see from your energy domain layout that this area is powered by com.example.panel-8-ldp6. So now you need to inventory all of the powered devices covered by the energy domain in that area. Then you can start to categorize them. The problem is finding out what these devices are. For the pilot, we
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| Chapter 6 Pilot to Production did a physical audit. Now what we suggest is to import or review audit data. Let’s take a look at some of the sources of that data.
Audit Data Usually, the word audit brings up dread, fear, and the formality associated with the IRS. The kind of audit we are talking about here is merely a listing of the things in your network and facility. With the convergence of facility and IT management, there is an opportunity to merge some principles from both disciplines and add a third for energy management. A facilities audit and a network audit are two traditional but very different types of audits. The good thing is that your managers will already have information for these audits. So what you want to do for your energy management rollout is to gather the information from facilities and network audits and then create an audit of your own for energy.
Tip Gather information from existing systems as much as possible. There is no need to track data from scratch for your energy management systems. Let’s a take a look at the information in these audits: Facilities Audit Data A facilities audit is a way to collect maintenance and performance information about a building. Typically, facilities audits produce formal documentation that evaluates and lists information about the following: •u Building foundations •u Mechanical plumbing and heating •u Cooling and ventilating •u Electrical service and distribution •u Electrical lighting •u Safety and fire concerns •u Security
Network Audit Data The purpose of a network audit is to document the components, connectivity, people, and processes involved in the operation of a network. Network audits are typically divided into two parts—the physical audit and the connectivity audit. The physical audit usually contains information about the following: •u Wiring closet locations •u Wiring •u Device types and manufacturers •u Administrator contact information •u Physical installed location
The connectivity audit typically contains information about the logical setup of the network. This information relies on the Open Systems Interconnection (OSI) model. As follows:
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•u Connectivity (OSI Layers 1–2) •u Redundant paths •u Logical connections (OSI Layer 3) •u Redundancy and security (OSI Layers 4–7) •u IP addressing and virtual local area network (VLAN) configuration
Note Most network audit information can be easily obtained by using the network management systems that operate your network. Typically, the network management applications have a network audit or accounting report capability. The same is true about facilities information from your building management systems. Energy Audit Data The Energy audit data is the same as the data described in Chapter 5 “Choosing the Data”. The audit should contain the information described in the following two classes: •u Entity Data Class •u Entity Usage Class
By looking at the information in the audits, you can populate your system with the information you need to get a good inventory. So let’s get back to our example. If you were working on the HR rollout area, you would go through the audit data and add devices to your system and associate them with the energy domain(s) for that area.
Tip Having submeters for the distribution points will give you the power usage for an energy domain. By inventorying the domains, you get the breakdown of that usage. When you go through the exercise of looking for the individual devices in that domain, you might not be able to find every device. Don’t worry about getting all the devices. Get as many as you can. If there are devices with which you can’t communicate, you can account for the gaps in your report generation. The next step is to go through the data and add additional information. The significant information to add is the role of the device, its importance rating, and any tags that can be used to search for the device. This adds the business context you’ll need.
Roles, Ratings, and Tags We introduced the concepts of roles, importance ratings, and tags in the previous sections. To briefly recap: Role A description of what the device is being used for in your business Importance Rating A rating from 1–100 indicating how critical the device is to your business Tag Arbitrary keyword you can use for grouping or searching for devices
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| Chapter 6 Pilot to Production Role After you collect the data in a rollout area, you can start to categorize the items. In a rollout area, you first set the role attribute for all the devices you added from your audit. The roles will vary based on your business type. The key is to come up with a namespace of roles, which is just a set of roles you will use. A good way to figure out a role is to select a device and make sure the role consists of two words that fill in the blanks in the following sentence: This device is used for providing __________-__________ services. The first word indicates the business context, and the second word indicates the type. It should answer the question, What does this device provide for my business? A bank of lights in a kitchen is not just providing light; it’s providing hospitality-lighting services. So its role is Hospitality Light.
Note The role is very closely associated with the type of device. By default, a device provides a service for which it was designed: phone for telephony, lights for lighting, and refrigerator for keeping food cold. What you are looking to do is to describe the device further. If you find yourself using terms such as location, you’re probably not giving the role business context. Every business has items with roles that describe services such as: •u Hospitality •u Business •u Data center •u Lab •u Office •u Public •u Waste
Taking the preceding roles, you can then start combining them with the type of the device to come up with two-word roles—for example: •u Lab cooling •u Office phone •u Hospitality light •u Public PC •u Lab router •u Data center switch
In addition to the general roles, you may have services that are specific to your industry, such as the ones listed in Table 6.1. The roles listed in the table can be combined with the type of device to come up with appropriate two-word roles. Table 6.1 lists some example business types
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and the services or departments within the business. The third column of Table 6.1 gives some selected examples of creating a two-word role for the devices in those business areas.
Table 6.1:
Example roles specific to lines of business
Line of Business
Business Services / Areas
Two-Word Role Examples
Education
Student, Faculty, Administration, Athletic
Student Phone Faculty Lighting Athletic Kiosk
Finance
Trader, Teller, Fulfillment
Trader Switch Teller Phone
Manufacturing
Retail
Assembly, Control, Inventory, Shipping.
Inventory Cooling
Advertising, Stock, Processing, Cashier
Advertising Display
Shipping Lighting
Stock Lighting Cashier PC
Support
Call, Assistance, Support
Call Desktop Support Switch
Medical
Patient, Admissions, Billing, Public, Examination, Lab
Billing Workstation Examination Lighting Lab Cooling
Importance Rating After you’ve set the roles for the devices in the rollout area, you can now indicate how critical the device is to your business. The importance scales we recommended are as follows: •u 90–100: Emergency response devices •u 80–90: Executive or business-critical devices •u 70–79: Average devices •u 60–69: Staff or support devices •u 40–59: Public or guest devices •u 0–39: Decorative or hospitality devices
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| Chapter 6 Pilot to Production When you combine the importance rating with the role, you can start to see how the business context takes shape. For example, if we have a set of three switches in a financial company data center, the role of data center switch is helpful, but it doesn’t tell which one is more important. We can assign an importance rating as follows: •u Data center switch, 90 •u Data center switch , 50 •u Data center switch, 50
If you have a set of phones, for example, you can easily pick out the emergency phones because they are the ones with an importance rating of 100: •u Lab phone, 100 •u Lab phone, 60 •u Shipping phone, 100 •u Shipping phone, 65 •u Public phone, 100 •u Public phone, 40
Tag With roles and importance ratings, we’re coming up with a language to describe our devices. We can start to read the data and know the business context and the importance of the device. If you want to categorize the devices further, you can assign tags to each. Tags are a free-form way of adding information. They’re keywords you can assign that will help you search and report on the devices. You can assign any set of tags—there’s no limit to the groupings you can create.
Tip Assign tags to devices for which you’d like to report data on later. For example, if you want to group a set of devices assigned to a team, you could make up a tag for that team and later report data on their usage. If you are looking at the devices in a building that are associated with a specific team, you could tag the devices based on the team owner or team name. For example, you could have an HR recruiting team. If you want to tag the devices used by the recruiting team, you would categorize them by role, importance rating, and add a keyword of recruiting. Table 6.2 provides a good picture of the devices used by the HR recruiting team. The team has some office lighting, phones, and PCs. There are two critical devices and a few low-priority hospitality devices. If we have similar tags for other teams, we can start to report usage by team or any other arbitrary grouping. Thus if we wanted to see a list of all the kiosks regardless of the team to which it was assigned, we could search for all devices tagged with the word kiosk.
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Table 6.2:
Example roles, importance ratings, and tags for a specific team
Device
Roles
Importance Rating
Tags
1
Office Lighting
65
HR, recruiting
2
Office Lighting
65
HR, recruiting
3
Office Phone
100
HR, recruiting, emergency
4
Office Phone
65
HR, recruiting
5
Office Phone
40
HR, recruiting
6
Public PC
35
Lobby, recruiting, kiosk
7
Office PC
65
HR, recruiting
8
Office PC
65
HR, recruiting
9
Common Lighting
100
HR, recruiting, emergency
10
Hospitality Beverage
20
HR, recruiting
Monitored Data and Baseline Now that you have the data in your rollout area, you can start to monitor the usage. This is where you set up the data collection feeds you designed and implemented in the pilot. If you monitor the instantaneous power consumption of devices over time, you can start to get the baselines. Let’s look at our HR recruiting data and add in the monitored data. At the same time, we can collect the power usage, as shown in Table 6.3.
Table 6.3:
Power consumption for HR recruiting team at 4 p.m.
Energy Domain
Role
Importance
Tags
Usage
com.example.building .panel-8-1
Office Lighting
65
HR, recruiting
1,000 W
com.example.building .panel-8-1
Office Lighting
65
HR, recruiting
1,000 W
com.example.building .panel-8-1
Office Phone
100
HR, recruiting, emergency
6W
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| Chapter 6 Pilot to Production Table 6.3:
Power consumption for HR recruiting team at 4 p.m. (continued)
Energy Domain
Role
Importance
Tags
Usage
com.example.building .panel-8-1
Office Phone
65
HR, recruiting
10 W
com.example.building .panel-8-1
Office Phone
40
HR, recruiting
10 W
com.example.building .panel-8-5
Public PC
35
Lobby, recruiting, kiosk
85 W
com.example.building .panel-8-1
Office PC
65
HR, recruiting
110 W
com.example.building .panel-8-1
Office PC
65
HR, recruiting
110 W
com.example.building .panel-8-3
Common Lighting
100
HR, recruiting, emergency
400 W
com.example.building .panel-8-1
Hospitality Beverage
20
HR, recruiting
120 W
As you monitor the data, you can compute a simple baseline by role, importance rating, or the tags you set up. Chapter 7, “Reporting,” will go into more detail about the data and presentation. What’s important now is to start to set up a baseline from the feed in preparation for setting up rules and policies.
Implementing Policies Policies are processes that you set up to manage power consumption. These policies will change over time. You’ll be tweaking and adjusting them as part of your routine maintenance. After you set up a rollout area with monitoring and a baseline, you can take three types of actions to influence power consumption: Passive These are purely monitoring steps. They are actions you take that do not interrupt or change the power consumption of devices. You are collecting information without changing the source. Manual These are steps you can take outside of an automated process. You can distribute reports and have your users take steps on their own to adjust their power consumption. Active These are steps that you can implement (scripts or processes) in your system to monitor and adjust power consumption automatically.
Tip Concentrate on the passive and manual activities first. Active controls require testing. Practice manually before you start something that’s automated.
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Passive and Manual We’ve mostly been dealing with passive activities. The monitoring data and baselines are all collected without interrupting the system. In network management, these are read types of activities. If you’re familiar with SNMP, these are the types of activities that you can provide from the read community of information. An SNMP read community defines a group to which devices belong for management. Devices with the same read community setting belong to the same group and will respond to requests from management stations in the group. The information available from devices for read access are defined in the SNMP Management Information Base (MIB) that the device implements. Network management stations can poll this information for passive network management. The same principle applies for energy management. As you roll out a building and tag devices, you can get creative in the types of grouping. When you assign ownership of devices to people or teams, you can set up reports and incentives. For example, if you produce a report based on team tags, you can assign a person who is in charge of that team and associate the savings to that person. You can produce a report of savings versus your baseline. Set up a leader board during your rollouts. It should show in real time who is saving power compared to their baseline.
How We Did It: Healthy Competition People want to reduce their power usage. We had teams with labs full of devices. After we were able to report power usage by team, the teams began to compete in friendly ways to reduce their consumption. We also had universities testing our products that established competitions between dormitories. Monthly winners got money to throw parties based on the dollar savings they could produce.
These activities are all based on the human and social element of your enterprise. These are passive with respect to the energy management system, which is not adjusting the usage. The usage is adjusted by reporting the data collected.
Active Active policies are those that you can automate via your system. You may have a centralized system such as IBM Tivoli or a BMS system from your facilities control. You could look at your inventory of devices and identify times during the day when you can power off devices. For example, a branch office of a bank is closed after 7 p.m., and no one is occupying the space except security. You could set up automated processes to shut off the devices at that time. For our rollout, we used Cisco EnergyWise to have the network act as the point of policy control. We set time-of-day policies on the switches that would send control messages to the attached device. We could power devices on and off via these control messages. If you have centralized systems for control of devices, you can set these up in those specific BMS and NMS systems.
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| Chapter 6 Pilot to Production As facilities and IP networks converge, we see distributed systems such as Cisco EnergyWise becoming more important. Distributed solutions can scale and be configured with information from local utilities.
The Bottom Line Review your pilot implementation. With your pilot implementation done, you have a chance to review the data and its presentation. Master It You’ll need to review your progress with your stakeholders. How do you ensure that you have the information you need for a rollout? Partition your enterprise into manageable rollout areas. You’ll need to select areas to which you will roll out your system within your enterprise. Master It Get to know your campus and start to think about what areas you will convert and when you’ll convert them. What areas and in what order will you roll out to production? Categorize power-consuming devices in a rollout area. After you’ve selected rollout areas, you’ll have to look at all the devices in that area and categorize them. Master It What are the roles and tags you should use in your rollout? Establish a range of importance ratings to rank power-consuming devices. You’ll need to have a set of importance ranges that makes sense for your business. They should be applied to all rollout areas. Master It What are the importance ranges that make sense for your business? Establish a baseline and set up manual policies. For each area you roll out, you’ll need to set up a baseline of power usage. After you set it up, you can devise manual incentives for the occupants of each area. Master It What is the baseline power consumption given your categorization? What kind of manual processes can you put in place? Set up active policies. Active policies can ensure that unattended power controls are enacted. Master It What systems can you modify to control power consumption? Are there areas of your enterprise that can be turned off based on the time of day? Are there any systems that provide APIs? Could you write scripts using these APIs?
Chapter 7
Reporting Nothing can conjure up an image of wasteful bureaucracy more than the word report. Mention a report card or the famous “TPS” reports in the movie Office Space, and you’ll get a response as if you’ve asked someone to do long division while waiting in line at the department of motor vehicles. Before the Internet and web browsers, the world revolved around reports. Hunks of paper as thick as phone books were delivered to your computerless desk. That was a time when information came to you! Today, you go to the information. You search, browse, and format data the way you want it. You are the captain of your information, and you ask for it when you want it, not the other way around. We expect information to be available and up-to-date when we call for it. Thus reporting for your energy management systems is not about the delivery of the information but about its collection, storage, and aggregation. You need to make sure that you are tracking information and have it ready so that when someone asks for it, you have it at hand. Those requesting this information may be your peers, your management, or eventually your government. In this chapter, you will learn the following: •u How to recognize government activities for mandated reporting •u How to use calculators for greenhouse gas equivalencies •u How to divide your data into manageable, time-based sets for detailed reporting
Information Review Kirk to Engineering...Mr. Scott, report! James T. Kirk When you think of reports in business, you typically picture tabular data that shows either time or money being spent. Dollar amounts and time are the common units used to measure any business activity. Looking at a business simplistically, labor is consumed over time, and dollars are the result. You can manage business activity on the input (labor and time) or output (dollars) side. In the energy management area, power is consumed over time and greenhouse gasses (GHGs) are emitted. Similarly, you can measure the inputs (power over time) or the outputs (GHG). Up until now, you’ve been setting up prototypes and production data for collection of the inputs. Now you’ll have to report the outputs as well.
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| Chapter 7 Reporting For the input side of the data, you need to know the business context of the consumption. In previous chapters, we went into detail about the information you needed to track consumption. We reviewed how to set up the business context for consumption and arbitrary groupings. For reporting, you’ll have to collect that input information over time. You’ll need a way to keep the data in appropriate time-related sets. You’ll also have to evaluate the output of consumption as a factor of GHG emissions. Not only is GHG a way to summarize consumption, it’s also the unit that governments will use to regulate energy. Your first step is to get a basic idea of how to calculate GHG emissions. You will take the data described in Chapter 5 in the Entity Class and the time series data in the Usage Class to create reports. You’ll have to setup the information in a database. Creating a database or modern data mine is straightforward. Most database management systems have reporting features, applications, and widgets for displaying the data. In this chapter, we focus on how to organize the data so that your reporting tools can do what they do best—show the data.
Government Mandates One trend is clear—governments are taking a more active role in regulating energy use and carbon emissions. The trend is to require businesses to report on their GHG emissions. Most governments have started with manufacturing companies, but it’s clear that this will eventually become a requirement for any commercial building or enterprise. What that means is that you will have to collect and report energy consumption and the energy source to some government agency. Not only does it make business sense to report and monitor your usage, but it also will eventually become a legal requirement. Let’s take a look at some of the current activities in the United States and European Union. A quick review will set the stage for showing how important reporting energy usage externally will become to your business.
Tip Government regulations and programs change over time. Familiarize yourself with them, but always remain focused on your core data. Detailed power consumption, source, price, and allocation information will always be needed in any internal or external program.
U.S. Activities The U.S. Environmental Protection Agency has issued regulations for reporting of greenhouse gas emissions: In response to the FY2008 Consolidated Appropriations Act (H.R. 2764; Public Law 110–161), EPA has issued the Mandatory Reporting of Greenhouse Gases Rule. The rule requires reporting of greenhouse gas (GHG) emissions from large sources and suppliers in the United States, and is intended to collect accurate and timely emissions data to inform future policy decisions. www.epa.gov/climatechange/emissions/ghgrulemaking.html The ruling, which was signed and became effective on December 29, 2009, listed 42 emission sources, of which 31 are required to report their GHG emissions. The idea is that the largest emitters of GHG will be monitored first. The result is that any manufacturer of vehicles or supplier of fuel that emits more than 25,000 metric tons of GHG must report and be monitored. These manufacturers in heavy industry will be reporting their emission and energy consumption to the agency in the form of a registry.
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Government Mandates 161
Just as you have created an approach to energy management in your enterprise, the U.S. government is setting up a way to monitor the biggest emitters of GHG—that is, the biggest consumers of energy. In fact, the EPA stated in the ruling that mandatory monitoring will help develop future policies and programs. This is in line with the general principle for energy management: Monitor the biggest areas first and then work on ways to reduce and manage. The White House has taken the initiative a step further and, through executive order, has mandated that all federal agencies undertake sustainability measures that will include similar reporting. The U.S. Department of Energy issued the following: Executive Order (E.O.) 13514; Federal Leadership in Environmental, Energy, and Economic Performance; was signed on October 5, 2009. It expanded upon the energy reduction and environmental performance requirements of E.O. 13423… Sustainable Buildings and Communities Federal agencies must enhance efforts towards sustainable buildings and communities. www.whitehouse.gov/assets/documents/2009fedleader_eo_rel.pd For the United States, these actions are at the upper and lower bounds of energy consumption for industry. The largest producers of GHG are required to report, but so are the smallest federal buildings. Eventually, all industries in between will be required to report their GHG emissions.
European Union Activities In 2002, the European Union agreed to the Kyoto Protocol: 2002/358/EC: Council Decision of 25 April 2002 concerning the approval, on behalf of the European Community, of the Kyoto Protocol to the United Nations Framework Convention on Climate Change and the joint fulfillment of commitments thereunder. www.europa.eu/legislation_summaries/environment/tackling_climate_change/l28060_en.htm There are five major portions of the Kyoto Protocol. Two of them are concerned with accounting and compliance, respectively. This means that the European Union will be asking their citizens, in the form of corporations, to report their consumption and GHG emissions to comply with the protocol. Reporting will be a key part of the activities, and businesses will have to implement reporting. Just as a country wants to see a breakdown of energy consumption within its borders, corporations will need the same breakdown within their corporate entities.
Chinese Activities The Chinese government has not released any recent reports. During the 2009 United Nations Climate Change Conference (COP15), the Chinese government released information that dated back to 1994. The Standing Committee of the National People’s Congress (China’s parliament) released a statement: Relevant departments and regions will form action plans and medium- and long-term plans to cope with climate change and mitigate greenhouse gas emissions, based on the targets and requirements set out by the State Council. Reuters “China says moving to enforce greenhouse gas goals” Sun, Feb 28 2010
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| Chapter 7 Reporting Although the statement is not as detailed as one would hope, it’s clear that China will eventually require reporting of energy consumption as well.
Emerging Nations and South African Activities The energy management concerns of emerging nations are a bit different. These countries are primarily struggling to keep up with demand. The price of energy might not be a factor as much as the availability and production in these countries. When demand for energy comes close to the available capacity, demand management becomes critical. This is peak demand management. As a result, the reporting and monitoring of energy will be more focused on capping use at critical times to avoid blackouts. Thus the monitoring and reporting efforts in these countries are focused on energy demand management. For example, the problems of South Africa are common among emerging nations. The Financial Times newspaper in the United Kingdom published an article in January 2008 outlining some of the problems in South Africa. Eskom, South Africa’s state electric utility, was reported as struggling to keep up with demand. The largest portion of Eskom’s electrical supply comes from burning coal. As a result, not only is South Africa the largest GHG emitter in Africa, but according to the Eskom 2007 annual report, the company will need to double its capacity by 2024. Eskom is having a hard time keeping up with demand, and rolling blackouts are common. The government and select corporations in South Africa signed a protocol in 2005 called the South Africa Energy Efficiency Accord. This agreement was set up to help open a dialogue for addressing the demand problems. Although the South African government did not require any monitoring or reporting, the need for demand management will make it inevitable. Emerging nations are primarily dealing with peak demand. This is similar to your peak demand management problems, though your issues are more likely related to pricing and economic reasons than availability.
Effectiveness of Government-Mandated Reporting The ultimate goal of reporting is to enact some type of change. Just as you are monitoring your energy consumption to reduce expenses and manage demand, governments are trying to do the same for their constituents. The data you report will eventually feed into cap and trade programs. Governments can mandate reductions, but cap and trade programs have proven more effective in the past. A cap and trade program is a system wherein economic incentives are provided to businesses to reduce consumption or production. For example, a cap and trade program was enacted by the United States in 1990 as part of a set of amendments to the Clean Air Act. The amendments set an emission cap and built in an incentive to exceed compliance. If you came under the cap, you could trade, sell, or bank the difference. While the amendments to the Clean Air Act were passed in 1990, the programs were started voluntarily in 1989. By the end of the last decade, there were significant measurable reductions in acid rain for the United States. More recently, the European Union established the European Union Emission Trading System (EU ETS), which created temporary trading periods for GHG emissions. As these markets mature, companies will have more opportunity to participate. If you look at the pattern of government activities outlined previously, you’ll see that most start with reporting first and then hoping that economic conditions will arise naturally to create incentives. The idea of cap and trade is to kick-start the conditions so they are manageable.
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Conversion to GHG and CO2 Equivalencies 163
You can think of this as managing forest fires. You have two choices: You could monitor and report on the undergrowth and rainfall and then hope that the next fire is manageable. More proactively, you could enact controlled burn programs to ensure that a larger crisis doesn’t arise. Cap and trade programs provide for controlled reductions now instead of waiting for better market conditions to arise later. Today we are at the early stages of detailed energy consumption reporting. Eventually, a more dynamic market will become the norm with the hope of spurring measurable reductions in GHG emissions.
Conversion to GHG and CO2 Equivalencies Regulators will want you to report the output of your consumption by using a standard unit of measure. This unit is typically either GHG or carbon dioxide equivalent (CDE or CO2e ). When creating a reporting system on the outputs of your consumption, you need to know the source of your power in order to compute the equivalencies. For example, if you know you consumed 1,000 kWh of electricity, you will need to know the source of the generated power to know the GHG equivalent. Was the energy you consumed created by solar, diesel, wind, water, or nuclear power? Because we are in the early stages of energy management, most calculations will use a default. Ultimately, you want to take the information on your consumption and cross-reference it with information from your utility on the source of the data. This is where the smart grid efforts intersect with your business. You need to know the source of power in order to fully correlate your consumption. One of the best ways of doing this today is by using the calculators and formulas provided by the U.S. EPA as a way to compute the outputs: http://www.epa.gov/cleanenergy/energy -resources/calculator.html. Eventually, you’ll have to incorporate these formulas into your report calculations.
Figure 7.1 U.S. EPA greenhouse gas equivalencies
Energy Usage in Kilowatt Hours Emissions in CO2
Equivalent Usage as CO2
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| Chapter 7 Reporting
How We Did It: Equivalency Calculations When we were creating Cisco EnergyWise to manage power consumption, Cisco also developed online calculators based partially on the U.S. EPA formulas to calculate greenhouse gas and carbon dioxide equivalencies. These calculators served as the basis for our reporting and management systems. Enter information about your company Select GHG Emissions for equivalencies
Energy Domain Breakdown If you had to summarize one of the primary missions of your energy management system, it would be to show the area under the meter curve. In previous chapters, you divided your enterprise into energy domains. These domains corresponded to power consumption units that you could measure. Up until today, the best you could do was to chart the metered usage. With the approach we outline here, you have the ability to get the breakdown of energy consumption without having to meter every device. Figure 7.2 shows the information you can get from a metered domain. The charting of a meter yields a pretty boring report. You can take the usage data and apply pricing and source information to get the GHG emissions for a given usage, but there’s no business context. The GHG emissions and costs will be associated with the meter and typically represent a cost to your facilities.
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Energy Domain Breakdown 165
Energy Domain Metered
Figure 7.2 A metered energy domain
100000 90000 80000 70000 Usage
60000 50000
metered
40000 30000 20000 10000 0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time of Day
You’ll want to break down the energy data into your business context. In Chapter 6, “Pilot to Production,” we detailed how to manage an energy domain and provide the business context information. You add role, importance ratings, and arbitrary business assignments to the data. Now it’s time to report on that information. Looking at the same energy domain, if we break it down it by the assigned role, we could come up with usage that fills in the area under the metered report. Figure 7.3 shows a role-based breakdown of the same energy domain. Energy Domain Details
Figure 7.3: 100000 90000 80000 70000 60000 Usage
An energy domain reported by role
50000 40000 30000 20000 10000 0
Decorative Wireless
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time of Day Telephony HVAC
Hospitality PCs
Lighting Networking
| Chapter 7 Reporting If you report your data based on keywords tagged with a business function, you can show the same information broken down by business context, as seen in Figure 7.4. Energy Domain Business Context
Figure 7.4 An energy domain reported by keywords for business context
90000 80000 70000 60000 Usage
166
50000 40000 30000 20000 10000 0
Common Executive
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time of Day Security Marketing
Common Engineering
HR Accounting
Now you can manage energy based on much finer-grained assignments. If there are spikes in the total usage as reported by the meter, you can determine who or what caused each spike. The prototypes, system, and rollout you’ve been working on all come down to these types of reports. In the preceding figures, you can see the usage for one day. As you maintain the information, you’ll have to account for time. In the next section, we cover how to partition the data into timebased sets.
Note If you look closely at the breakdowns by business context, the value might not add up exactly to the metered curve. That’s okay because the slope of the curves is what matters. You’re trying to estimate the values of the meter readings. There will always be some loss or values you can’t measure. Low-resolution visibility is better than no resolution.
Live, Operational, and Historical Data As IT network management and facilities management converge, we can take some lessons from one discipline and apply them to the other. Network administrators have dealt with performance data from the inception of the discipline. Over time, some basic divisions of concerns arose based on some simple use cases. When you are tracking information about network devices, you usually approach it from three different contexts: •u Sometimes you want to look at a device immediately and examine what’s going on with
the device in real time. This is typically in response to some immediate diagnostic need or
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in response to a fault condition. Examining the device like this in real time is often intrusive. You’re asking the device to provide you information in real time. It’s like attaching a probe to the device. •u At other times, you want to view what’s been going on with a device in near-immediate
time. You might want to view the device for just the past few hours or during some business-related time period. For instance, if you’re a bank, you might want to see what has been going on in the past banking period or day. This is typically not intrusive. You’re not attaching a probe—you’re just looking at near-term data. •u For planning or auditing, you might want to look at device information over a long period
of time. You might want to examine the behavior of the device in the past month, quarter, or year to see if there are trends you can exploit or resolve. You might want to compare data from one year to another to look for seasonal trends.
Tip Treat energy reporting like performance reporting in network management. If you were reporting on the CPU usage of a device, you could look at immediate readings, operational readings with business context, or historical trends. The same holds true for power consumption. Thus with these types of scenarios, you’ll want to arrange your data so that you can respond to requests from those contexts. More-formal definitions of the sets are as follows: Live Data Real-time or diagnostic data typically reported only on demand and used to diagnose a problem or for an immediate trend. Operational Data Data collected regularly by a device that is typically not intrusive to collect and is part of the device’s normal recording operation. The data is typically not available for a long period of time, and it is stored on temporary media. Historical Data Operational data periodically archived into separate long-term data stores, which can be compressed or expanded to provide trends.
Note The concept of live, operational, and historical information has been applied to other nonrelated areas. If you’re familiar with digital video recorders (DVRs) such as TiVo, you can see these same concepts. You can watch a live feed, see the most recent half-hour of information, or view recorded shows. That’s the same division of live, operational, and historical data that network managers have been using. The data elements stored in these sets typically differ only by time period. The presentation of the data as well as the location where it is stored will be very different. Live data is typically reported in a dashboard fashion. Typically, it’s not stored. This data is usually streamed directly from the device to the presentation widget or report. The very concept of a dashboard is to mimic a car or airplane. You’re looking at the immediate readings, not the history. The information is live. Compare that to historical data, which enables us to see trends over long periods. That data is typically charted and graphed with much less granularity. Operational data is typically stored on the device and is not kept for a long period of time. The data contains some recent history. Historical data is operational data that has been archived. When you are creating reports or responding to requests for data from your users, first determine whether the information is live, operational, or historical. Then use a format that is typical for that type of data.
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| Chapter 7 Reporting Tip The data with which you populated your database in Chapter 5, “Building a Pilot Deployment,” was described as entity and usage classes of data. This should be the baseline for all time-based data. Whether you reported it immediately, for a short period, or saved it for historical purposes, use the Entity and Usage fields. To make the data historical, you simply add applicable timestamps and store each reading in different storage locations for live, operational, or historical indication.
How We Did It: Getting Feedback Early When we created Cisco EnergyWise, we started with mock-ups of the reports and user interfaces we wanted. We got feedback from showing other teams the early mock-ups. Then we designed the data and the means to get the data both live and operationally. We left the historical trending of data to the management systems. To aid in the development of these management systems, we created a software development kit so our partners and other vendors could develop reporting systems. We took the information, added simple business context, and were able to create very sophisticated reports.
Various vendors have merged the IT and facilities information that is outlined in this chapter. Some examples you might want to examine are shown in Figure 7.5, Figure 7.6, Figure 7.7, and Figure 7.8.
Figure 7.5 SolarWinds Orion Reprint Courtesy of SolarWinds Worldwide, LLC, © 2010 SolarWinds Worldwide, LLC
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Figure 7.6 IBM Tivoli Reprint Courtesy of International Business Machines Corporation, © 2010 International Business Machines Corporation.
Figure 7.7 Cisco EnergyWise Orchestrator
Figure 7.8 CiscoWorks LMS Reprint Courtesy of Cisco Systems Inc, © 2010 Cisco Systems, Inc.
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| Chapter 7 Reporting The Bottom Line Recognize government activities for mandated reporting. You’ll need to be familiar with the compliance and regulatory requirements for your business. Master It How do you find out which reporting regulations apply to your business? Calculate GHG emissions by converting usage. The data you collect is usage data broken down by business context. The usage data can be converted to GHG emissions. Master It How do you convert usage to GHGs or CDEs? For example, if a department you identified by keyword reported using 2,300 kWh of electricity, what was the CO2 equivalent? Divide your data into time-based sets for detailed reporting. The data you want to track is described as the entity and usage data in Chapter 5. You’ll need to know how to partition it into manageable time-based sets. Master It What types of data should you track over time? If your stakeholders want to budget based on time, such as financial quarters, last hour, or peak usage, how would you report the data?
Chapter 8
Administering Energy Domains To date, energy administration has been mostly done via Building Management Systems. These systems help you manage physical buildings. As energy management becomes IP based, you’ll need to look beyond the borders of a physical building and start to manage energy as a broader community of devices. You will have to administer devices by classifying them according to their use, not just by the location where they are plugged in. You’ll have to create policies based on that use and according to the business context of the devices. Administering some devices will require complex policies, while others can be administered by simple schedules. Over time, you will have to specify and enact your policies and track how they are applied to your network. In this chapter, you will learn the following: •u How to organize your energy domains into a hierarchy •u How to classify your devices according to consumption type •u How to classify and create a master list of policies •u How to track the way that policies are fulfilled in your enterprise
Organizing the Energy Domains The sublimity of administration consists in knowing the proper degree of power that should be exerted on different occasions. Charles de Montesquieu When a technology becomes IP based, you will see paradigm shifts in how it is administered as the technology expands. IP-based technologies will inevitably increase the scope of what you are managing. The scope increases as you become connected with a broader community. This is a natural progression you can see everywhere, even in life itself. For example, when you are a small child, your world is very limited. Your world consists of your room, playroom, or house. As you get older, your world gets a bit bigger, and you expand to the neighborhood. As you mature, you expand your world farther to include your school and city. You might go to college and venture farther, and soon the entire physical world is accessible to you. Technologies mature in very much the same way. As technologies become internetworked, the areas of concern expand to encompass the entire world. Electronic mail progressed from messages within a single mainframe computer to multiple computers within a company and then to the entire world as the technology became IP based. The administration of email had to change along with it.
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Like parents raising a child, IT administrators had to learn how to change their rules and administrative requirements as the technology expanded. They had to deal with different challenges and loosen their control of others as the technology matured. Administering energy is going through the same transition today. Up until now, managing energy was tightly coupled to the physical topology of the electrical deployment. Facility administrators manage energy with Building Management Systems. The very name shows how tightly coupled the system is to the physical deployment. You are managing a building. Just as parents have to adapt as their child grows up and goes away to college, building administrators will need to let go of the concept that control of energy is mapped to the physical building structure. Good IT administrators can switch from thinking about the network infrastructure as a physical topology to a cloud of services that has no real border or boundary. In network management and administration, this comes down to managing the network physically and, at the same time, as a broader cloud or set of services. IT administrators manage the physical structure of the network based on a physical building deployment, but a significant portion of administration does not map to any physical layout. For example, let’s look at security on a network—specifically, network admission control. This area of network management deals with who is able to connect to the network and what privileges they are granted after they are connected. When looking at the physical layout of a building’s network, the network might look like a typical hierarchy, as shown in Figure 8.1.
Figure 8.1 Hierarchical network with energy domains
Access
Distribution
Core
Distribution
Access
Data Center WAN
Internet
But for admission control, one single building becomes less significant, and the network begins to encompass a wider area without a physical border. The question then arises, Do you continue to administer based on the buildings, or do you let go and recognize that the network has no physical borders? If you look at the evolution of IP-based technologies, the answer is clear. Remove the borders.
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Figure 8.2 Hierarchical network campus
Campus Services Core NCA NCA
Distribution
Access
If you look at typical campus design, such as the one shown in Figure 8.2, the physical buildings are not as relevant as the network connectivity and layout. For a global company, the campus or enterprise can be worldwide. So administering security and admission control based on the physical layout is irrelevant. The administration is more closely tied to the user. Is this user able to connect to the network? The user’s location might serve as input to the decision, but the physical structure of the building is barely relevant.
Cisco Borderless Networks Cisco recognized that, as Internet technologies become the basis for enterprise activities, physical borders become less important. Cisco launched a program in 2010 called the Cisco Borderless Network. The main technologies that support this program are video, security, energy, and wireless. Cisco Medianet, Cisco TrustSec, Cisco EnergyWise, and Cisco Motion are product offerings that support those technologies respectively. It is no coincidence that energy is one of the main technologies in the program as energy management is emerging as a discipline unto itself that has no physical borders.
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As energy administration becomes IP based, first you have to look at the energy network from a physical topology standpoint just for basic administration. Next, you have to recognize that there are many more areas of administration than services on the IP-based network. Administration of energy comes down to providing policies, or rules, that should be applied to the physical and nonphysical portion of your enterprise. The upcoming section looks first at how you can organize energy domains based on the physical structure for basic administration. Next, it describes how the energy use can be used to classify policies that would not map to the physical structures. In fact, as energy management systems evolve, the policies will map closer to the user or social use of the network. Today, with the rise of social networking sites, the physical topology and connections of the network are not as relevant as the connections among the users.
How We Did It: One Size Doesn’t Fit All When deploying pilot systems, we found that a one-size-fits-all classification scheme was not practical. Different areas of Cisco have varying use patterns. For example, lab equipment can be used heavily by an engineer working varied hours. The engineer might work late nights or need the equipment to stay running for long testing periods and long-running simulations. In those situations, we had to tie the usage to the engineer’s presence or reservation. In contrast, lobby areas were in use during daylight hours, and the use of customer support areas was based on shift work. Add to that data centers, which could vary use based on market conditions, and you have a very diverse set of policies. Any solutions would have to accommodate user-based policies, as well as business and facilities policies, unified in one energy domain. The concept of free-form tagging in Cisco EnergyWise and the CALO lab reservation system was the end result.
Performing Physical Energy Domain Administration In previous chapters, we described an energy domain as a set of devices that roughly equate to a metered area of energy consumption in a physical building. For facilities managers, this winds up being very close to a set of devices that are all consuming power from the same distribution panel. For network managers, an energy domain is roughly the same as a Layer 2 broadcast domain. A broadcast domain in networking is a set of connected devices that can all be reached by a broadcast over the same data-link level. Separate broadcast domains are typically linked together via routers, and when these are connected, local networks become a larger connected network. For IP-based energy management, each administered energy domain could roughly equate to one electrical distribution panel. Just as a typical building can contain sets of data broadcast domains, your energy administration can contain sets of energy domains. After you have your buildings’ partitions into sets of energy domains, you should create a larger view of the domains to encompass your campus or global network. This will provide a view of your enterprise as consisting of physical domains but managed as an entire cloud. This is exactly how network management deals with the scale of data networks. Next, you have to decide which tasks should be administered on the physical topology versus those that can be managed over the topology.
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How We Did It: Creating an Energy Broadcast Domain When designing Cisco EnergyWise, we knew we needed a solution that could grow just like networks. With networking, you can expand subnets, LANs, or VLANs by adding more devices. We created an energy domain that contained a broadcast mechanism for reporting and controlling energy. We took the data network and made it energy aware by adding a broadcast protocol built directly into the switching and routing products. In that way, as you expand your network, you are also expanding your energy control network. Just as you can broadcast information within a Layer 2 domain, with Cisco EnergyWise you can broadcast energy command and control messages in a single domain, albeit over a Layer 3 protocol. By connecting these domains similarly to how networks are connected, you can achieve a scalable solution based on your existing network infrastructure.
Tip Whenever possible, we suggest mapping your data network to your physical electrical distribution system. Your network does not have to map to the distribution system directly, but keeping them organized similarly will help you use the network to manage your power usage. In network management, the FCAPS model (faults, configuration, asset, performance, security) gives us a set of concerns for classifying administration tasks. You should take any administration task that you have and first determine into which category it falls. Then you need to decide whether the task is local or global to the enterprise. Typically, specification of a policy, or rule, is something that you do globally, but configuring that policy is something you do locally. For reporting purposes, the task of data collection can be done locally, but the correlation, summary, and reporting of the data are done globally. Table 8.1 shows examples of some administrative tasks that you could attribute directly to the physical domain (local) versus the entire enterprise (global). Global activities can be mapped to a set of local activities. For example, taking a look at security, you could have access control policies for employees based on their title or job function. You could compile lists of user IDs globally and create access lists that are enforced at local sites. Similarly, you could have energy use policies created globally but enforced or enacted locally.
Table 8.1:
Examples of local and global administration activities
Category
Local Activity
Global Activity
Faults
Alerts reported to BMS/NMS
Alerts correlations
Configuration
Policy setting
Policy specifications
Alert threshold setting
Alert threshold setting
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Table 8.1: Category
Examples of local and global administration activities (continued) Local Activity
Global Activity
Device tagging
Tag type specification
Accounting
Collection of usage data
Aggregation, summarization, and reporting of usage data
Performance
On-demand and short-term data collections
Scheduled data collection and aggregations
Security
Access control list setting
Access control policies
How We Did It: Storing Policies in the Network When we first implemented policies by using Cisco EnergyWise, we created simple rules by taking a global view of the sections we were going to affect. We then translated the rules into schedules that we stored locally in the network. We later customized the rules for specific users in the sections. Our policies were limited to simple schedules and SNMP traps that are available in Cisco EnergyWise. For example, if we determined that a device connected to a specific port on a switch needed to be configured as “On” only between the hours of 8 a.m. and 4 p.m., we could enact that policy via EnergyWise configuration commands directly on the switch: time-range power_on periodic daily 08:00 to 16:00 end interface f0/17 energywise level 10 recurrence importance 100 time-range power_on end
When this policy is configured on the switch port, anything attached to the interface receives the message for that policy action. Thus if a PC running the EnergyWise, agent or a facilities device implementing the EnergyWise protocol was attached to that port, that PC or device would receive the same policy action. The policy is stored in the network, waiting for a connecting device to control.
Classifying Energy Consumers After you realize that most administration of energy takes place across physical boundaries, you can begin to think of how to manage the various endpoints. You’ll still use the IP network to administer energy, but ultimately you’ll be setting policies for endpoints. You’ll have to determine how to handle those endpoints.
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One way of doing this is to classify the devices. You can look at each endpoint in your enterprise as a generic energy-consuming device. From there, you can set up categories to classify the devices. If you’re familiar with object-oriented design and programming, you will recognize the principle. In object-oriented design, elements are classified in a type hierarchy. Each type or class has subtypes. The subtypes complete the sentence Is a type of. For example, if we look at the category Mammal and we had the subtype of Zebra, we could complete the sentence as Zebra is a type of mammal. If we tried that with Parrot, the phrase Parrot is a type of mammal would obviously be incorrect. So what we can do is have a basic classification hierarchy for our endpoints and devices in our enterprise. Table 8.2 shows a sample hierarchy arranged as a tree with EnergyConsumer being the root with eight leaf nodes. If we take one of the leaf nodes in the table and construct a phrase, we can see whether the hierarchy is valid. For example, A workstation is a type of fixed EnergyConsumer.
Table 8.2:
Hierarchy of consumer types
EnergyConsumer Transient Reserved Lab
Fixed
Walk-in PC
Mobile
Data center
Workstation
Hospitality
Environmental
Network
The following is a description of each consumer type outlined in Table 8.2: EnergyConsumer This is the root of the classification of consumers. It is the most generic type in the classification scheme. Transient Consumer A transient consumer is a type of energy consumer that is typically not stationary or can be in use based on a policy or user request. Reserved A reserved consumer is a type of transient consumer that could request to be used at a certain time or that could be part of a pool of devices that are used for a specified period of time before being returned to the pool. There is typically a predictable or specified time that the device will be in use. Lab A lab consumer is a type of reserved consumer that is used by an individual or group for testing or experimentation and requires a reservation before being used. Walk-In A walk-in consumer is a type of transient consumer that can consume power at any time. No up-front reservation is required, and the period of use is not specified beforehand. PC and Mobile PCs and mobile consumers are a type of walk-in consumer that can consume power for a period of time while charging or in use. Fixed Consumer A fixed consumer is a type of energy consumer that typically is installed and is closely associated with the physical topology or building. The usage pattern is typically closely associated with the location.
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Data Center A data center consumer is a type of fixed consumer that provides computing and network services. Workstation A workstation consumer is a type of fixed consumer that is closely related to the location. These stations are used based on their location, which does not typically change. For example, a bank teller workstations or trading desk workstations are fixed locations. Hospitality A hospitality consumer is typically a shared appliance for providing food services, decoration, or information capabilities—for example, a coffeemaker or digital sign. Environmental An environmental consumer is a fixed consumer that provides heating, cooling, lighting, or other physical services. Network A network consumer is a type of fixed consumer that provides data and information services or access—for example, switches, routers, and wireless access points. You can further define subtypes in this hierarchy to suit your enterprise. For example, you could further divide environmental consumers into HVAC and lighting subtypes.
Applying Classifications to Devices As you might recall, we defined a database of energy-consuming devices in Chapter 5, “Building a Pilot Deployment,” and Chapter 6, “Pilot to Production.” That data had an attribute called keywords. The class or subclass names outlined in the type table can be assigned to the device’s keyword field in order to classify it. This classification can then be used for determining a policy for the devices.
How We Did It: Classifying with Cisco EnergyWise With Cisco EnergyWise, we were able to classify devices in our test networks by using keywords. The keywords enabled us to tag endpoints, switches, and interfaces according to the classifications we outlined. We could set keywords to indicate our classification. We used the same principle of creating our classifications globally, but when implementing it locally, we recorded it directly in the network. For example, the following EnergyWise keyword commands could classify the interface of a specific switch by its use: int fa1/0/17 energywise keywords Hospitality end int fa1/0/17 energywise keywords Workstation end
The tagging and classification took place locally, but the creation of the keywords and the analysis took place globally.
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Your plan for energy administration will be to create policies. First you will have to specify the policies or rules. Then you will have to associate them with the class of devices you want to administer. This is very much like an object-oriented system. In an object-oriented system, an object (device) is categorized into classes (consumer types). Then methods (policies) are associated with the class. The next section describes how to specify policies.
Specifying Policies Administering energy comes down to specifying a set of policies for energy consumption. A policy is a rule or set of rules that dictates when consumption of power should take place or whether consumption is authorized. After you have classified your devices, you can start to set up these policies to indicate when the devices should consume power. Some policies are straightforward. You could just have time-of-day rules indicating when devices are in use. For example, you could have a policy that says that a set of workstations should be on from Monday to Friday, 8:00 a.m. until 9:00 p.m. Other policies might need some feedback or more variables to work. For example, a policy could state that a device should consume no more than 200 watts at any one time and for no more than 300 kWh per week. Some policies might be strictly enforced, while others might just be reported as exceptions. One of the most complex types of policies occurs for reservation-type devices. These are devices that are placed in a pool and can be reserved or checked out for use for a specific time period and then returned. A lab is the most common reservation type. Most other types of devices can be initially controlled by time-of-day polices and later by using more-complex policies. The following list takes a look at these policies and gives examples of each. The goal is for you to map the policies to the devices you’ve categorized in a way that makes sense for your business. You can then enact those policies by using the systems available to you. The best approach is to create a list of policies or rules and then give them a name. After you have names, you can then assign policies to classes of devices in your enterprise.
Tip You don’t need to create every type of policy for every type of device right away. For example, if you see that the majority of your energy consumption is coming from fixed lighting and that a time-of-day policy will work, it’s best to focus on that class of device and policy first. The policy should be associated with the keywords you assigned, not the physical device type. The keywords enable you to mix device types. For example, you don’t want to assign a policy to a phone made by a specific vendor. Rather, you’ll want to assign the policy based on the keywords used to classify the device in your business context. The different types of policies are explained in the next sections. :
Static Policies Static policies are the most basic rules you can set for a class of devices. These typically encompass time-of-day rules that can be expressed as a schedule. For example, you can define some policies with one or more schedules, such as the example shown in Table 8.3.
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Table 8.3:
Static policies
Name
Rule
workday
Level 10, 8 a.m. M–F Level 0, 8 p.m. M–F
off-peak
Level 10, 12 a.m. Level 0, 4 a.m.
reboot-weekly
Level 0, 3 a.m. Sunday Level 10, 4 a.m. Sunday
standby
Level 10, 8 a.m. M–F Level 3, 8 p.m. M–F
Dynamic Policies Dynamic policies are a bit more complex in that the rules might involve checking one or more systems for referenced information to determine whether a policy should be enacted. For example, you could have policies like those shown in Table 8.4.
Table 8.4:
Dynamic policies
Name
Rule
cap-usage
If usage > 300 W then set to Level 3
cap-total
If weekly usage > 300 kWh then set to Level 3
notify-max
If weekly usage > 300 kWh then issue trap
Enforced Versus Suggested Policies After you have created your list of policies, you should indicate whether the policy is enforced or suggested. An enforced policy is one in which you attempt to control the device directly. With a suggested policy, you might simply check to see whether the device is adhering to the policy and then indicate when the device is not complying. After you have defined your policies, you could wind up with a master policy list like the one in Table 8.5. This master policy list also denotes to which classes of devices the policy applies. By mapping policies to specific classes, you can effectively administer the devices in your enterprise. You’ll be able to derive the policies that apply to the device based on the classification set forth in the keywords. Table 8.6 shows examples of polices applied to devices.
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Table 8.5:
Master policy list
Name
Rule
Enforced
workday
Level 10, 8 a.m. M–F
Y
Level 0, 8 p.m. M–F off-peak
Level 10, 12 a.m.
Classes Hospitality, Environmental
N
Level 0, 4 a.m. reboot-weekly
Level 0, 3 a.m. Sunday
Y
Service
Y
Workstation, Service
Level 10, 4 a.m. Sunday standby
Level 10, 8 a.m. M–F Level 3, 8 p.m. M–F
cap-usage
If usage > 300 W then set to Level 3
N
cap-total
If weekly usage > 300 kWh then set to Level 3
N
notify-max
If weekly usage > 300 kWh then issue trap
Y
Table 8.6:
Data center
Examples of policies applied to devices Keywords
Derived Policy
65
HR, environmental
workday
Office lighting
65
HR, emergency
com.example.building .panel-8-1
Office phone
100
HR, emergency
com.example.building .panel-8-1
Office phone
65
HR, service
standby rebootweekly
com.example.building .panel-8-1
Office phone
40
HR, service
standby rebootweekly
Energy Domain
Role
com.example.building .panel-8-1
Office lighting
com.example.building .panel-8-1
Importance
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Table 8.6:
Examples of policies applied to devices (continued) Keywords
Derived Policy
35
Lobby, workstation
standby
Office PC
65
HR, workstation
standby
com.example.building .panel-8-1
Office PC
65
HR, service
standby rebootweekly
com.example.building .panel-8-3
Common lighting
100
HR, environmental
workday
com.example.building .panel-8-1
Hospitality beverage
20
HR, hospitality
workday
Energy Domain
Role
com.example.building .panel-8-5
Public PC
com.example.building. panel-8-1
Importance
Applying policies can be automated, but even if they are applied manually at first, it is a onetime rollout task. After policies are in place, the maintenance is much easier.
Implementing Policies After you have mapped your list of policies to your devices, your task will be to control your devices based on these policies. The current state of the art for energy management systems is that varying systems will control the devices. PCs can be controlled from a PC management system, lighting can be controlled by a BMS system, and some devices might have no controlling systems at all. Until the convergence of energy management to a fully IP-based system arrives, you will need to keep track of your policy and classification assignments in a custom energy management system like the one we described in the previous chapters. The policies can be labeled according to how you implemented them, but the actual implementation might need to be done in the specific management systems. Following are some ways you can automate or provide policies: Centralized Scripting You could develop scripts that enact a specific policy. As parameters to the script, the specific device access information can be used to enable the script to run against the device. This is the most manual approach. You could also maintain a source code repository of scripts that you can schedule from a network management station and that use SNMP or BMS protocols. Network-Based Configurations Cisco EnergyWise has the capability to store time-of-day events in the configuration of the switch or router that issues these events to attached devices.
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Your time-of-day policies can be mapped to scheduled events and configured on the switch or router. NMS- and BMS-Based Policies Network management systems with energy capabilities can be used to set policies in the NMS or the switch itself. Similarly, your BMS systems can offer scheduling policies. After you have identified how to fulfill the policies for specific devices, you should track them along with the policy assignments. You can track them by denoting how the policy was fulfilled for that device and policy combination. Keep a table similar to Table 8.7 to track these policies.
Table 8.7:
Example policies fulfillment tracking
Energy Domain
Role
Keywords
Derived Policy
Fulfilled by
com.example.building .panel-8-1
Office lighting
HR, environmental
workday
BMS
com.example.building .panel-8-1
Office lighting
HR, emergency
BMS
com.example.building .panel-8-1
Office phone
HR, emergency
EnergyWise
com.example.building .panel-8-1
Office phone
HR, service
standby rebootweekly
EnergyWise
com.example.building .panel-8-1
Office phone
HR, service
standby rebootweekly
EnergyWise
com.example.building .panel-8-5
Public PC
Lobby, workstation
standby
NMS
com.example.building .panel-8-1
Office PC
HR, workstation
standby
NMS
com.example.building .panel-8-1
Office PC
HR, service
standby rebootweekly
NMS
com.example.building .panel-8-3
Common lighting
HR, environmental
workday
BMS
com.example.building .panel-8-1
Hospitality beverage
HR, hospitality
workday
Manual
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Administering Energy Domains
How We Did It: Cisco CALO Lab Management Before we embarked on creating Cisco EnergyWise, there were various efforts throughout the company to manage energy. Everyone recognized that lab-type equipment was by far the greatest consumer of power at the Cisco campus. Even without a detailed audit, it was apparent where we needed to focus our attention. The only way to manage such a large number of devices was to recognize that these types of devices were transient and needed a reservation system to control when they were in use. Our IT and facility teams came up with a reservation system known as CALO. This system allows for devices to be placed into pools and for users to sign out devices for a specified period of time. While the devices are being used, the power consumption is recorded and allocated to the user. After our pilots with CALO and Cisco EnergyWise, we started combining the systems by using the lessons learned from these efforts. The biggest gain came from classifying the devices and identifying where the hot spots in our campus resided. When we identified the hot spot as the labs, we found that no system existed to manage labs. After identifying where the biggest gains could be achieved, we embarked upon creating systems to administer them.
The Bottom Line Organize your energy domains in a hierarchy. After you’ve identified the energy domains in your campus, you will need to organize them. Master It How do you create hierarchies of energy domains? Classify your devices according to consumption type. You’ll need to create a set of classifications for your consumers of power. Master It What are the different devices in your enterprise? Master It What are the classification types for your enterprise? Classify and create a master list of policies. You’ll need to create a master list of policies and determine how to fulfill them in your enterprise. Master It What types of rules can you apply to your classified types?
Chapter 9
Making Your Program Sustainable After you’ve deployed your pilot, you will start to get some cycles back as you track the results of all your hard work. To make effective use of the two quarters in which we ran our pilot, we shifted our focus to developing the back-end program management. As we mentioned in Chapter 1, ”A Stake in the Ground,” many marketing-driven Green programs fizzle out because they are not built on a foundation of scalable technologies and financial incentives. If you want your energy management program to grow, you will need to ensure its business relevance and structural integrity. In order to help you build a strong business foundation for your sustainability program, in this chapter you will learn the following: •u What funding models best apply to an energy management program •u What programmatic models best support the back-end management of energy as a service •u How to ensure that an energy management program can scale across the business
Funding Your Program A leader is the wave, pushed ahead by the ship. Leo Nikolayevich Tolstoy We have found that most of the larger organizations with which we’ve worked have a Green program in their marketing or public relations department. This is to be expected when there have not been many easily adoptable technologies within the IT space. At Cisco, we already had a Green disposition across our facilities and IT teams. Our job was simply to “operationalize” the technologies our engineers were already developing. It is in the deployment of several technologies that you will see big savings. Although it is fine to have a Green marketing program, you will need additional funding above and beyond what we have seen in many marketing programs. Because Green is such a broad term, the first thing we did was narrow the scope of the program we proposed to the corporation. When it comes to funding a formal program, or even a pilot, you will need to be very specific about what aspects of Green IT you will address in most cases. In our experience, there was no clearer economic and environmental case than focusing on energy management. Saving watts will almost always reduce greenhouse gasses. After you’ve narrowed the options your company can take to reduce its energy use and/or invest in local, renewable power generation, you will need to choose a funding model that can scale. This funding model is, of course, tied to the technology and operations on which you are focused. In almost every case, the technologies that are practical are applicable to facilities and IT operations. Keep in mind that the tie-in with your finance team is a good aggregate point, as discussed in earlier chapters.
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| Chapter 9 Making Your Program Sustainable Programmatic Funding Models We considered several options to fund our program. In this section, we cover the ones we considered and discuss the cost benefits we saw in each. Keep in mind that when we started our program at Cisco, the funding model was slightly more complex because we were also building new products and professional services capabilities. We are taking the approach in this book that the technology is at a stage that any homegrown work on your behalf will be limited to systems integration and program administration. Given that municipal and utility incentive programs vary greatly across the globe, please consider the models we discuss here as a guide only. You will likely need to tweak the models we used to fit your specific local tax and incentive programs. However, the savings you get from reduced energy use will be universal. In this section, we describe the difference between these basic savings and any additional incentives you might find from utility providers or local governmental programs.
Performance Contracting Models Performance contracting is a fairly mature methodology employed by large facilities vendors such as Johnson Controls, Schneider Electric, and Siemens. These vendor-provided building services offer a reduction in energy use through building-efficiency projects. These projects typically cover actions such as moving to high-efficiency lighting, improving insulation, installing new windows, and so on. At Cisco, our Workplace Resources (WPR) group has been working with real estate services companies since 2006 to implement these types of projects. These building performance improvements provided a 10 percent reduction in our total energy use within the first three years of deployment. This translated to a savings of $15 million for our financial year 2009. This savings required 2.5 full-time heads in our WPR teams for just over two years’ time. Rob Rolfsen, Cisco’s director of sustainability, along with the help of senior managers Andy Smith and Sheikh Nayeem, drove these projects to fruition. Although they represented an organizationally separate team from ours, we worked closely with them, and they were our staunchest supporters in the company.
How We Did It: Organizing Energy-Reduction Efforts Initially, we found it preferable to split energy-reduction efforts between facilities and IT functions. Although there is a lot of overlap in efforts to reduce energy usage, all of the traditional performance contracting approaches can be done by facilities teams without IT’s help. Data center cooling is an obvious exception, but for the most part these efforts can be run out of a facilities function without your involvement. However, as your program matures, you will find opportunities to bring these efforts together. Our plan is to bring these teams together to form a new, cross-functional energy management team. We are now concentrating on correlating thermal maps, location, business importance and, where applicable, system resource loads (CPU utilization, memory usage, disk, and network IO), and we are coming up with a single but modular energy-monitoring and controlling infrastructure to facilitate this goal.
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Whether you call it performance contracting or not, this model seeks to have any resource investments funded by an agreed-upon percentage of total savings. This is an ideal programmatic structure in that it is a small net new cost to the business for a short period of time. Think of the cost here as a short-term loan. The total loan amount is determined by the resources required to achieve a savings target. The term of the loan is dictated by how long it takes you to get through the proof-of-concept and pilot phases. After you are through these phases and are ready to establish your program, you will begin to track real savings centrally. These savings enable you to pay off this initial “seed funding” that got you through the early phases of your program’s development. As the program moves forward, the funding becomes simple—you and the other teams (IT and facilities) that are working with you get a percentage of the savings added to your top line. In our case, we worked out a negotiated savings between facilities, IT, and our program team, as shown in Figure 9.1.
Figure 9.1 Basic structural overview of how savings can be shared across functional teams to provide energy-reduction incentives.
IT Services Compute Network Storage Other
Corporate Finance Asset Billing Occupancy Billing Energy Billing Rebates and Incentives
Energy
Savings
Real Real Estate Services Services Critical Facilities Corporate Real Estate Maintenance Security Transportation
Asset Management
Facilities Management
Capacity Management
Building Management
Control Policies
Reporting
Control Integration Policies
Energy Management Services
Traditional IT Service Models Depending on your executive support and the progressiveness of your IT operation, you might be able to introduce energy management as a typical IT service. Just like email, payroll, and voice services, energy management can be a new service IT offers to the various business functions in your organization. In our case, we only started positioning energy as a service once we had a pilot program foundation built. In 2010 at Cisco, 63 percent of our energy use went to lab operations. Because labs are by definition dynamic, loosely managed environments, we needed an opt-in option. This option enables our lab managers to self-manage their labs’ energy use. On the other hand, we have office space and data centers making up the remainder of the company’s energy use. For these environments, a performance contracting model was more attractive because it is better suited for centralized management. With an IT service model, most IT operations will charge a business unit a monthly fee to establish and maintain the hardware and software required to support a particular application. This is typically a standardized process within an IT operation and one into which you can
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| Chapter 9 Making Your Program Sustainable simply insert your energy management program. However, IT will most likely need help in testing, deployment, and back-end administration. This is where you and the team you build can start your program as a new function within an IT operation. In our case, we are working with Cisco IT on an IT service model at the time of writing this book. Our program is slightly different in that we have the core competency for energy management services within our Customer Advocacy professional service teams. At the time of this writing, Cisco is “in-sourcing” skill sets and program management elements from these professional service teams. If you choose to take the approach of using a traditional IT service model, you will still need to demonstrate the business case for doing so. You shouldn’t have to build this anew because there should be existing templates from other IT services that you can leverage. Using these templates might require some small changes, but for the most part you can show savings back to the business units through the IT operation on a monthly basis. Be sure to check with your finance teams to ensure that your business case is using current data and that you present it within the current context.
Managed Service Models Also referred to as remote monitoring service models, managed service models provide a turnkey energy management service with no staffing requirements from facilities or IT departments. As an example, many users will have a vendor or systems integration partner manage their network uptime for an annual fee. This fee is often less than what it would cost to hire new staff inhouse to manage a particular IT service. At the time of this writing, there are very few options in the industry for turnkey energy management services. Although Cisco does plan to launch these services in the spring of 2011, we do not offer them today. The partner with whom we worked that does provide these services in the United States now is a company called Quality Attributes. This company provides customized dashboards, database integration, and comprehensive real estate and IT energy monitoring. Although basic monitoring will get you some savings by simply exposing energy information, it does not bring about the savings we are achieving in the 20 percent range. However, we are working with partners such as Quality Attributes to provide common monitoring and control for clients across a range of environments today. The norm in the next three to five years should be energy management services coming from large IT vendors such as Cisco, HP, and IBM. These will likely be paired with the current performance-contracting services portfolios from large facilities vendors. When paired, these two service approaches should bring total savings capabilities into the range of 30–35 percent across the enterprise. Yes, there is that much waste in enterprise-level energy usage today! For some operations, you might determine that simply having a partner manage and report on energy use is the way to go.
Determining Program Placement By now, you’ve picked up on the fact that in this book we are advocating an insertion strategy with your program’s placement. Said another way, energy is a very horizontal service, and building a new standalone, vertical program will hinder scalability and increase complexity. When possible, look to integrate your program into existing facilities and IT programs. If none exist, then you will create a new program, but it will be a program that is fully integrated into the current operations stack.
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At this point, you should be looking at the technology available on the market and skill sets within your organization to determine where you should integrate your program. In our case, we had deep expertise in facilities, IT (software and hardware), and utilities technology and operations. Our core team included fewer than five people, but the skills mix we had enabled us to form what looked like a cross-functional facilities and IT team. This worked well for us in that we had a deep understanding of the technologies, operative dependencies, and project management dependencies to pull it together in a Tiger Team model. This virtual Tiger Team used elements of all three approaches we addressed in the preceding section. Unless you are working for a company that is developing new technologies, you might find it difficult to closely replicate our energy management program. Therefore, we recommend you consider one or all of the models we described in the preceding section. As part of this consideration and the organizational mapping we described in earlier chapters, it should become clear where you should look to insert your program.
IT Operations In this book, we are advocating that you focus on the IT infrastructure first and then look to work on facilities assets. Again, this is because the IT electrical load drives thermal loads that facilities infrastructure supports. As you trim energy use across IT assets, you will get a corollary return for facilities assets through thermostatic controls. However, we separate energy management for facilities assets from general building-efficiency improvements (lighting, insulation, Green roofs, and so forth). If your interest is in establishing a new IP-based energy management program, your IT operation is the best place for your program to live. We opted for a blended approach for our program’s deliverables. We are currently using a diverse mix of existing technologies across computing, networking, and storage assets. Furthermore, we did not limit ourselves to energy management. As the name of our Cisco Efficiency Assurance Program indicates, we look at energy management as one of many technologies that can help our operations run more efficiently. This includes virtualization, energy-efficient designs (data centers), and active management technologies. Figure 9.2 shows how we’ve structured our program’s placement across our Facilities, IT, Finance, and Customer Advocacy teams. An overlay model for our program gave us the most flexibility in technology selection and funding sources. In terms of where you might place the program within a specific IT domain, this is both technological and political. If you are to take on the program’s leadership role, the program should be at the level of director of energy management services. At this level, you will have the ability to manage what can be conflicting technologies across network, computing, and storage. If a director-level role is not practical within your organization, you can start the effort at almost any level. However, if your program is to scale up, a director level of investment is required in the long term. If you are at the level of domain manager (computing, network, or storage), you will likely be in a perpetual debate over which domain has the best approach to meeting energy-reduction targets. If you are like most clients with whom we’ve worked, you will find that IT is the most likely launching pad for your program. There are many reasons this is the case, but the great equalizer is that IT systems can scale much better than building systems today. IT systems do this primarily because of IP enablement. IP networks are far more advanced than building management networks. Some might argue that using database-driven engines such as OSIsoft in conjunction with building mediators such as Cisco can provide a comparable scale to an IT system, and they are correct. However, it’s important to understand that you will heavily leverage an IP-based network in this model to achieve this scale.
Inserting and aligning your program at the right levels is critically important to its ability to choose from diverse technologies and to generate funding to deploy them.
Figure 9.2
Design Specifications • CAD, Visio • SketchUp
Plumbing • Refrigerant • Air Economization • Fire Prevention
Electrical • UPS • PDU
Mechanical • CRAC • CRAH
IP-Enabled Energy management Program
Program Focus
Vendors Engaged • Business Consultant • Real Estate Mngmt • Site Architect • Professional Services
Roles Engaged • VP, Dir Facilities • Mgr Facilities and BMS • Mgr Data Center • Architect DC • Mgr Building • Architect Network
Special Project Management
Energy Efficient Design
Capacity Management
Services
Legal
Cadence
IT Interoperability
Operations
Cabling
Structural
Electrical
Mechanical
Structural
Electrical
Mechanical
Competency
Design Specifications • UPS and Genset • Electrical Dist • CRAC and Air Dist • Spatial and Racks • Availability and Resilience • Efficiency • Structured Cabling • Access and Security
Capacity Specifications • UPS • Electrical Dist • CRAC and Air Dist • Spatial and Racks • Design Capacity
SLA
Milestones
Alignment
Cadence and Delivery • Operative Efficiency • Governance • Deliverables and Dates • Indemnity Ownership • Contractual • Project Management • Service Levels
Operative Alignment • Standards • Metrics Regulatory, Compliance • Legal • Indemnity Structure Tiering
Power and Data
Floor and Racks
Power
Cooling
Floor and Racks
Power
Cooling
Deliverables
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A lot of advancement has occurred in building management systems and database-driven data normalization that is approaching the level of a typical enterprise IT system. This is the reality today, and you have many protocols, interfaces, service levels, and risk considerations with which to contend. You can mitigate this complexity by implementing solutions that rely on technology that scales well. Building too many standalone, platform-specific solutions will increase your program’s total costs and take longer to implement than consolidated, overlay solutions. This is at the heart of efficient IT, and it will likely push you toward placing your program in an IT function at some level.
Facilities and Real Estate Operations For the sake of this book, we are describing a real estate operation as one that provides full portfolio management for all the buildings of a business. This differs in level from a facilities operation, which is typically a subset of a real estate operation. The facilities function within a larger real estate context focuses on specialized applications within the larger building portfolio for the business. A good example of this is a building manager versus a data center facilities manager. The latter will have more experience with IT simply because that person works more closely with IT operators than would a building manager. Therefore, in this section we assume that a facilities management function will have a better understanding of IT systems and technology than a real estate manager. If you hope to leverage existing skill sets within a facilities or real estate operation, you should work with professionals who have the most familiarity with IT systems and operations. Capacity management and data center facilities management within a larger real estate operation are good places to start. These teams work almost exclusively with IT operations and should have a decent understanding of IT hardware platforms. Working with these teams will save you time in educating the host team on what your project aims to accomplish. Adopting energy services such as IP will also be less disruptive to these teams than to a higher real estate operations team. Although there are various approaches to energy management that rely on Building Management Systems (BMS) to achieve savings, we do not cover these approaches in this book. In our experience, a BMS approach works well for an individual building but not for an entire enterprise. Generally speaking, BMS networks do not scale as well as IP networks, so if you do place your program within a facilities function, do not limit yourself to using only BMS technologies. The converse also holds true. If you start your program using IP-based technologies, look to scale your program in later phases to incorporate facilities technologies.
Tip Be careful not to push an IT systems management construct too aggressively with a facilities operation in your initial, positioning engagements. Not only are building management systems and IP-enabled systems different technologically, but there is also a “cultural” divide between facilities and IT. This cultural divide is exacerbated when an IT person adopts a position of IT can do it all. Focus more on your project and program methodology and business case to mitigate the perception of disruption. If you do decide to bring your program into a facilities operation, recognize that you will likely need to “in-source” some IT skill sets. Depending on the technologies you choose, you will primarily need network and computing administrators within a facilities energy management team. Furthermore, you may need a person who is adept at database administration, software platforms integration, and reporting methodologies. These skills will complement the mechanical, electrical, and spatial design skills typically found in a facilities operation. If you can get support and funding to bring IT skill sets into a facilities operation, placing your program here should work fine.
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| Chapter 9 Making Your Program Sustainable Choosing a Program Structure Remember that form follows function. We wrestled with the best way to provide guidance on the right program structure for energy management in this book. On one hand, we didn’t want to be so detailed that a program structure that worked for us would need to be shoehorned into your paradigm. On the other hand, we wanted to be detailed enough that you could pick and choose from elements of the program structure that worked for us. What we provide in this section is a list of programmatic elements with commentary on the benefits and challenges of each. Take what works for you and leave what does not. Even if your program becomes fully integrated, you will likely need to run the program for a period of time as a standalone entity. This typically takes place between your pilot completion and the program’s integration into an existing service framework (IT or facilities). During this period, it is important that you structure your program either to scale on its own or easily integrate into existing operations. This is the organizing principle for this section—a centralized structure versus a distributed programmatic structure. The latter relies on a virtual team model as described in earlier chapters. We considered a number of questions in determining how to structure our energy management services team. Table 9.1 shows some of these questions. Some were technology related, but the vast majority had to do with organizational dynamics. As we’ve pointed out in this book, the technology will take you only so far. You will need to assess your organization to determine what skill sets reside in what teams and whether it is structurally viable to centralize these skills or keep them where they are at present. Whether you centralize or distribute your program, the same basic skill sets will be required. In most cases, we found that our clients needed to hire in, or in-source, talent to fill gaps even after aggregating all available energy-related skill sets in their organizations.
Centralize It Think of new centralized programs as precursors to new business units like city-states. Where and when does a city-state begin and a province end? Furthermore, how does it happen? First, a region becomes populated and produces something that is of value (let’s say wheat). After this region produces enough wheat, it makes a profit. As this profit is reinvested in the civic infrastructure of the region, the region becomes more efficient and develops new forms of profitability. A snowball effect ensues, and the region evolves into a new, autonomously producing region. Only then does it move from a region to a city-state. At this point, a city-state has a “place at the table” to engage other citystates and develop centralized management in the form of a king or president. To a large extent, you will need to sell your programmatic structure in advance of a pilot for seed funding. Trying to sell a centralized approach without an executive directive is like selling the king on providing your region with all the benefits and considerations that a city-state enjoys before this city-state has produced anything. This is a tough sell, as you might imagine. It can be much easier if you have executives who are monetarily and professionally invested in an energy management program. In our case, we had executive support across many functions, but a centralized program didn’t make sense for how our teams were structured. We also had the right skill sets in the right places, so we saw it as unnecessarily disruptive to advocate for a centralized team. This is a phenomenon somewhat unique to IT vendors, but the distributed innovation our organization embraces worked well for us.
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Table 9.1:
Questions for determining the best programmatic structure to deliver energy management services
IT Operations
Facilities Operations
Finance Departments
Is IT asset management currently centralized?
Is facilities asset management currently centralized?
Are facilities and IT assets billing currently centralized?
What degree of standardization exists?
What degree of standardization exists?
How often are assets refreshed? Are IT teams billed for power?
How are energy and real estate capacities managed today?
What formulas are used to estimate and bill for electrical usage today?
Do IT teams pass costs on to business units?
Are IT teams billed back for power?
How is energy managed today?
Are any energy-related costs passed on to other operations?
Is desktop shutdown software currently deployed? Is computing or storage virtualization underway in data centers? How many IT assets are deployed and administered today? Are there efforts already underway to consolidate, virtualize, and better manage the efficiency of IT assets?
What types of building management systems are currently deployed? How many buildings are managed today? What was the total energy usage and cost thereof for electricity last year by building?
What kWh rates apply to what buildings globally? Is there one person who analyzes both facilities and IT asset billing trends? What is the billing structure for energy use across labs, data centers, and office space environments? Is there currently a cost estimate placed on the carbon intensity of provisioned fuel sources? How many buildings are managed today? What was the total energy usage and cost thereof for electricity by operations last year?
Assuming you can get executive support to establish a new city-state the day after your pilot concludes, the team you build will work toward scaling the functionality and in turn the savings your program will deliver. Following the preceding city-state example, if your region produces value, it will naturally evolve to a point where it has a seat at the table. An iterative approach is the option with the lowest risk and lowest capital expenditure. However, it moves more slowly and runs the risk of small returns due to resource constraints. Considering which functions to centralize and when will be a balancing act across need, cost, and time.
Distribute It Most IT operations today are accustomed to working virtually and across “party lines”; network teams need to work with computing teams who need to work with storage teams. However, facilities teams are less accustomed to this work style. Yes, mechanical and electrical teams need to work with one another, but the technology in that space moves like the tortoise when compared to IT. This is a cultural paradigm, but one of which you must be aware when considering your program’s placement and structure.
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| Chapter 9 Making Your Program Sustainable If you do decide to place your program within the management responsibilities of an IT operation, a distributed model should serve you well. This is not to say you can’t centralize later. If your program is delivering consistent and compounding value, a central program should be a real option in the future. These are decisions that can be made later, but determining what programmatic structure will help you best sell a pilot and beta program needs to be worked out up front. In our case, we arrived at the decision of going with a distributed program. Knowing what skill sets and cycles people have in your organization might even provide the answer to your program’s structure. If you don’t have the knowledge or the cycles from key contributors, you may need to jump straight to a centralized structure to some extent. In our case, we focused on building a cloud-based system. This is not the only way to go for a pilot, but we recognized the need for organizations to make independent decisions. This required us to build a system that kept checks and balances so as not to violate any existing domain-specific policies. In the long term, we will plan for having an infrastructure that will support role-based policy administration—that is, IT will set policies for IT devices, whereas network administrators will set network policy, and workplace resource teams will come up with the lighting policies in the lobbies and corridors in different buildings required by the business. These global policies will flow through the hierarchical energy domains and will be applied to the right kind of end devices, hence making it easy to bypass the centralization/distribution phase.
Pay for It There is an emerging option that should quickly follow the publication of this book. Managed energy services that seek to be the IT equivalent to the aforementioned performance contracting models appear to be just over the horizon. As these emerge, energy management will be opened to a much broader range of users. Under this model, you don’t need any specialized skill sets or centralized programmatic structure; you simply pay a partner to achieve energy savings. At the time of this writing, we saw no turnkey capabilities in the industry for managed energy services. What we did see were all the pieces of energy management technologies coming together one by one. We’ve seen HP acquiring a mission-critical facilities firm, IBM developing its own rack-based cooling technologies, and Cisco acquiring building-mediation technologies. Google and Microsoft are committed to energy-efficient data center design, and almost every client with whom we speak has some form of Green initiative underway. All these points indicate an existing demand for managed energy services—so the questions is when, not if. If we assume that these types of managed energy services will start to become available in late 2012, how can you prepare in the interim? Even if you outsource your energy management program, you will still need a small team to work with the partner providing these services. This model is similar to how you pay a utility today. Your energy management program “skeleton” team will need a level of skills comparable to those of the individual who currently manages your utility contracts. The baseline knowledge you gain from reading this book and following the self-study we recommend would directly prepare you for managing the contracts with a managed energy services provider. We cover more on what you can do to prepare for emerging technologies and services in the next chapter.
Program Scalability Now that you’ve reached the programmatic summit of your long climb from a proof of concept, you should be seeing many more peaks ahead. You will see so many new peaks that you will need a map and compass to prioritize new expeditions. Don’t forget what brought you here to
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begin with: sound data that is iteratively improved, business case analysis, organizational alignment, and a sound process. Use these assessment attributes to determine which new areas make the most sense to focus on. The good news is that you should have at least three years of scalability for your program by just focusing on IT and facilities assets. It will take you that long to establish full control of assets across data centers, labs, and office space environments. Be sure to spend some time analyzing your adoption, and project the amount of savings you can achieve and when. This is, in essence, your budget planning process to expand your program’s capabilities. After you have a handle on this trajectory, you can start looking to new application areas. In many regions of the United States and some countries in the European Union, financial incentives exist for using energy more efficiently. The majority of these are functionally rebate programs. Integrating the savings you gain from managing IT assets into the real estate teams that have access to these incentives is another funding source. Your value proposition to these teams is simple: “We can save you money, and you don’t have to do a thing….But we will require a percentage of those savings to be allocated back to our team to cover the maintenance costs of our program.” This approach gives you indirect access to the savings that institutional and governmental programs provide. You simply apply the same percentage of cost savings to incentive programs that you do to straight kWh savings. Said another way, you get a rebate on the rebate. We go into more detail on what new technology application areas might be of interest in the next chapter. In this section, we are focusing on the strategic, programmatic, and organizational elements of sustaining your program in the first one to three years. Within the first year, you should be able to establish your program within an existing IT or facilities operation. In order to do this, it is important to show a vision and methodology that will expand your program over time and meet key targets.
How We Did It: Cisco’s Customer Advocacy Lab Operations There are many diverse physical components, communications protocols, and software platforms to consider when it comes time to deploy an energy management solution. Our approach was to develop energy management technologies that played to our team’s core strengths across facilities, network management, and software development. It became clear to us that an energy management platform should be as ubiquitous as possible to achieve the largest savings. Luckily for us, we work for Cisco, which by some estimates provides as much as 70 percent of the IP backbone for the World Wide Web. Our strategy continues to be to develop a comprehensive energy management system that integrates energy discovery, monitoring, and control across IT, facilities, and eventually grid assets while integrating the following capabilities: •u Energy discovery capabilities for devices attached to Cisco networks •u Energy-aware clients loaded on devices that allow assets to identify themselves and commu-
nicate their operative power requirements •u Common monitoring and control across all organizational assets including both IT and facili-
ties assets
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| Chapter 9 Making Your Program Sustainable •u Logical energy domain management across physical assets and buildings •u Hierarchical domain structure for ease of monitoring and management •u Aggregation and association of IT assets with critical facilities assets through Cisco Building
Mediation technologies •u Secure transportation of data from all IP-enabled components to a central location to provide
a complete view of enterprise energy usage •u Automated polling and control policies for assets provided through logical energy domain
(micro-grid) constructs •u Common interface with and data normalization for supervisory control and data acquisition
(SCADA) networks, including utility and building management systems As the first phase of our three-stage program was built out, we conducted a pilot. Our pilot was run across 11 buildings and focused on large lab operations. As of June 2010, Cisco’s Customer Advocacy Lab Operations (CALO) teams have achieved a 33 percent savings in electrical costs in calendar year 2009 through implementing Cisco Energy Management Services. The CALO project scaled from 3 technical support labs to 11 labs worldwide. Dave Katz, manager of CALO, was the driving force behind this deployment. “Our goal was to bring 18,000 devices under power management, across 90,000 square feet of lab space, which when fully deployed will result in a $4 million savings in electrical costs annually,” Katz said. CALO labs represent only 4 percent of Cisco’s total lab space. As we expand and improve our approach to all Cisco lab environments worldwide, the estimated power cost savings increases to $20 million in the first full year of program deployment if we can deliver a conservative 20 percent total reduction. CALO initiated its energy management program in part to meet the power-density restrictions imposed by new lab space requirements. Like many data center environments, these labs are being asked to do more with less power. Katz and his team assessed their options to consolidate their labs to open up new power capacities. Only a handful of options were available to them. Developing and deploying Cisco energy management technologies was the front-runner, with virtualization being a somewhat distant second. “We found that virtualization could gain us double-digit electrical savings, but only across computing and storage,” Katz said. “Energy management, by contrast, is projected to save us 33 percent of our total electrical usage per lab when fully deployed. Of course, we are using both technologies to meet operative constraints while driving down costs.” The energy management system that was developed needed to accommodate the operative conditions of a lab. Lab equipment is typically tied to somewhat finite, single-function tasks and, therefore, can be shut down when not in use. The challenge CALO saw was how to associate an asset to a user and then implement basic policies to provide on-demand power to assets when needed and power them down when not. The answer came in the form of using a lab scheduling and reservations system to orchestrate the control policies along with an administrator-driven checkout system. The first major challenge in this pilot was the lack of common monitoring and control capabilities. This system needed to integrate more than 18,000 IP-addressable devices including servers, Cisco switching and routing, storage arrays, various appliances, and test tools while scaling the management of IP-enabled, in-rack “smart” power distribution units (PDUs) that were deployed. The second challenge was the ongoing task of mapping all assets to all power outlets.
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The solution was to integrate a Cisco CALO-developed asset checkout and reservation system to manage the asset inventory of these devices now under energy management. This system enabled easy mapping of devices, assignment of default power settings, and the scheduling of shutdown commands. According to Katz, a real challenge of relying on PDUs for control has been “to scale PDU deployments to address a very real day-to-day IP-address management problem, to maintain a consistent mapping of power policies at the chassis level, and to develop and maintain a power management maintenance and governance process that can be used by IT operations staff worldwide.” After the first three tech support labs were up and running, we shifted our focus to enable energy management across the remaining eight CALO labs in Europe, Australia, China, Korea, and Japan. Cisco labs worldwide account for two-thirds of the company’s energy use, and the CALO system represents the first time Cisco is using its own technology to meet its commitment to reduce greenhouse gas emissions by 25 percent by 2012. The solution that was implemented covered the following: •u Deploy asset-mapping tools that can discover energy usage across different operating systems •u Deploy IP-enabled, in-rack PDUs and the mapping of assets to the outlets for each unit •u Deploy web-based management tools to enable orderly remote power on/off through auto-
mated policies •u Integrate power automation with a device checkout and reservation system and database
historian •u Report on power usage by device, including the cost of power by user, department, asset, and site
This solution provides an energy management framework that delivers automated, user-aware policies that address energy control for thousands of devices lacking native energy-control capabilities. Using the network as the platform for monitoring and controlling energy, CALO’s webbased management tools were deployed and have enabled the establishment of energy domains, the assignment and execution of shutdown policies, and the publishing of cost and carbon dioxide equivalency (CO2e) savings. The goal is to move all not currently productive lab equipment to a powered off state to achieve a 33 percent cost savings in energy. We came up with this 33 percent estimate not as a shot in dark, but because we can see the power curves across groups of assets. When you see a flat curve, that asset is not currently productive. By tracking these assets for six weeks, we had an accurate estimate of the savings we would achieve before we ever deployed a control policy. This same approach enabled us to deploy in data centers to get IT efficiency benchmarks. In order to achieve these full savings and mitigate the IP-address management challenges that come with using PDUs for control, CALO is now working with Cisco Energy Management Services. This development partnership includes product and services management from Cisco EnergyWise, Building Mediator, and Smart Grid business units. Cisco’s Facilities and Energy Management Services practice within Cisco’s Customer Advocacy group is providing the operative expertise to bring these solutions through the labs and into our customer environments. The beauty of our approach is that in the next phase of our program’s development, the program will be software-based and backward compatible. For the first time, our clients will be able to manage their energy use related to IT directly at a very low price point and with very little disruption to the current business model. The CALO labs are composed of roughly 2,900 racks of equipment. Each rack was fitted with one IP-addressed PDU that was attached to a Cisco network. To make the overall job of IP-address management easier, controllers were connected to blocks of 48 PDUs to cut down on the number of IP
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| Chapter 9 Making Your Program Sustainable addresses to manage. Every chassis was assigned a default power setting that allowed the execution of orderly shutdown scripts via SNMP. A web-based checkout and scheduling management tool, the Omni tool, was developed to enable IT staff to assign power automation policies, manage case workflow, and schedule the deployment of shutdown commands to the PDUs. CALO is using a series of energy dashboards that enable IT management to track the amount of energy being used and to report on cost savings. By gathering information from the smart PDUs, the CALO energy dashboard reports on the following: •u Energy usage by product family •u Energy usage by device (entity) •u Real-time electrical draws by product •u Total electrical costs by IP address •u Estimated total cooling savings •u Electrical consumption curves throughout the year •u Total savings derived from the energy management system
This deployment is an industry breakthrough, and it is a proof point of Cisco’s efforts to incorporate energy as yet another network-based service. We found that, even with using PDUs, the break-even point was only 1.39 years. As our agent-based approach is maturing and we are eliminating the dependency on PDUs, our break-even point is expected to be less than one year. Correlating energy usage to the IP address is the foundation for the monitoring, measurement, and management of a more energy-efficient IT industry than we know today. As this approach scales up, we plan to build out more and more native, software-based capabilities to achieve the same level of management without PDUs. This will be accomplished by building on the foundation we established with Cisco EnergyWise in January 2009.
Vision and Execution At this point, you have officially moved past your pilot and are into the program’s inception. There is where your vision starts to propagate in a mitosis-like process to cover different groupings of infrastructure. You might remember from earlier chapters that we described a pilot strategy of deploying within low-risk environments such as labs. Now the same vision you articulated for labs can be extended into office spaces and eventually data centers. In essence, you will keep the same top-level vision statement throughout your pilot and program—that is, reduce wasted watts. However, it’s at this point that you take that top-level goal and drive it down to an execution plan. This execution plan will be the gel that brings your vision and deliverables together. We were careful to keep our deliverables realistic and riskaverse in nature. Even a small mistake involving energy at this point can be a major risk to your program’s sustainability. We opted to keep our execution plan simple and directly measurable as much as possible. Each deliverable was assigned to a program team member and served as that person’s manageby-objective (MBO) goal. In the case of a virtual team model, these MBOs will serve only as guides unless the manager of the contributing individual is willing to adopt your program’s
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MBOs. This is where the type of programmatic model you adopt will have an impact on your ability to enforce these MBOs. If you keep your execution plan simple and measurable, it should make it easier to garner support over time when you have enforceable objectives as part of your program.
Tip It is important to properly balance the long-term vision you have for your program with near-term deliverables. Going too far in either direction will limit the stakeholder base your program can serve. The broader your stakeholder base and the broader your funding sources, the more sustainable your program will be.
Methodology Logistically speaking, your methodology after program inception is similar to what we covered on benchmarking in Chapter 2, “Benchmarking.” Now that you’ve deployed instrumentation for common monitoring, you have a more accurate measurement of these benchmarks. At this point, your methodology simply focuses more on control than monitoring. Control of what? Again, go back to the IT hardware platforms you benchmarked in Chapter 2, and the IT asset energy allocations becomes a “paint by numbers” exercise. Said another way, after you have insight into power usage by hardware platform, you can assess an energy management technology and the level of effort to achieve a particular savings for a platform. During the first year of your program’s inception, you will likely be focused on building programmatic and technological capabilities for your team. This will keep you busy in building the following capabilities within your program: Energy Domain Construction and Administration A large company of 50,000–70,000 employees often has as many as 1 million IT assets deployed. Approximately 30 percent of your time might be spent structuring and maintaining logical energy domains to actively manage these assets. As stated in earlier chapters, we opted to use a blend of real estate and financial organizing principles in how we structured our energy domains. Asset Scheduling and Reservations An orchestration engine of some sort will be required in order to manage and deliver power policies. We kept things simple and used basic time-of-day and user-presence policies. The former simply turned assets off during non-office hours. The latter was tied into our lab administration system that is already used to “check out” assets to perform particular lab operations. We spent roughly 30 percent of our time working on this level of systems integration within the existing scheduling and reservations system. Database Integration A range of database options will be available to you for storage, data normalization, and reporting. In our case, we opted to integrate our storage data requirements into the most prevalent asset-reporting database already in use by our IT operations. This insertion approach enabled us to limit the cycles we would have otherwise spent on database creation and administration. We spent roughly 20 percent of our time integrating energy data into existing IT databases. Automated Reporting Capabilities Basic automated reporting is fairly straightforward and should not require much time. These capabilities are inherent to almost every energy management technology on the market today. However, we spent close to 20 percent of our time “plugging in” our data to the many existing reports across our company. Data center, lab and office space operators, business units, and executive leadership all had an inter-
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| Chapter 9 Making Your Program Sustainable est in the data our program provided. Time spent inserting your value into other teams’ reports demonstrates your program’s relevance and opens up new funding sources. As you structure your energy domains, a clear, macro-asset profiling will be needed. You will need to break out the many IT assets your company maintains into a structure that accounts for availability requirements (risk), departmental allocation (cost), and billing period (time). These domain attributes coupled with physical location will give you all the macro-elements you need to structure and develop the management construct. The real estate filter to the construct will have cost implications region by region (kWh costs, taxes, utility rebates, and so on) but will also provide a physical structure to replicate. We opted to keep our domains structured to reflect buildings and then aligned risk, cost, and time attributes to a building context. In terms of the methodology to build your team out over this first year, we followed the assets. Because we started by primarily networking assets in our pilot and early program production phases, we brought on network managers. As we transitioned into a heavier focus on computing and storage, we brought those skill sets on board and so on. This also applies to years 2 and 3, when you would start focusing on facilities assets. What will need to be consistent throughout are the programmatic and software development resources required to bring the assets together for reporting. The methodology we used of spinning in asset-based resources as needed while maintaining core programmatic, architectural, and software resources enabled us to run the programs with only four full-time people during the first year. This is now scaling up, of course, but we were able to get the program off the ground with only four people possessing the right mix of skills. For more-sophisticated systems, you need to integrate device location and building and campus maps along with temperature, humidity, business importance and, if applicable, system loads. It is common to have Radio-frequency identification (RFID) implanted in every asset. Combining this information will result in a state-of-the-art solution that not only enables monitoring and management, but also lets you ramp up to load prediction and causal effects of dayto-day energy usage.
Executive Support If you are an executive or have the direct support of an executive who has a passion for Green, take note that program sustainability requires executive sponsorship. The approach articulated in this book provides the strategy, methodology, technical guidance, and real-world guidance to achieve a 20 percent reduction in total energy use across an enterprise. A 20 percent electrical reduction across your enterprise is the Greenest thing your company could do today by far if your interest is in curbing global climate change. The only other technology in IT that even comes close is virtualization, which is applicable in only certain environments. These points need to be made clear to your executive leadership in both an economic and environmental context.
Tip Through your benchmarking activities, you will be able to determine the amount of energy your company uses today. As an exercise, take the energy information you uncover and convert it into not just cost and CO2 savings, but into cars on the road or any other analogies that will grab executive attention.
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The Bottom Line Determine what funding model is the best fit for your organization. You will need to fund an energy management program in a way that will ensure your program’s sustainability. Choosing a funding model with limited disruption to the organization and limited complexity is preferable. Master It Meet with the business leaders across the IT, facilities, and finance departments. Ask which options addressed in this chapter are most interesting to them. Establish a basic financial model for your program, include projected savings, and present it back to these leaders to gain confirmation of their support. Implement a programmatic model that can scale over time. The structure of your program will be as important to its sustainability as a funding source. Your team’s form will follow its function, so take care to map the right skill sets to the deliverables of the team. The biggest cost of your program will be staffing or professional services if you can’t find the right skill sets internally. Choosing the right structure will be specific to your operations but, where possible, keep it simple yet extensible. Master It As part of your interviews with business stakeholders to uncover funding sources, also look into existing organizational structures and operative models. Build a simple list of the pros and cons for the structures detailed in this chapter. Take this same list and project it three to five years out and see what investments are required to scale it.
Chapter 10
Preparing for the Next Big Thing With a secure program in place and budgets set, you can turn your attention to the bigger picture. At this point, you and a small core team of people will need to decide where the program is heading three to five years out. This level of strategic thinking and planning is a logical extension of the topics we covered in the preceding chapter. However, you have ample room for innovation and leadership in deciding the future of your management program. At the time of this writing, we are in this phase of futures assessment, so what we cover in this chapter is theoretical. What is practical is our application of the same business and technology criteria to the resource management options we are considering. Please view the content in this chapter as our opinion on how energy management technologies will evolve and integrate. With that in mind, in this chapter you will learn the following: •u What adjacent technologies and business priorities might impact or be affected by an
energy management program •u How user behavior and governmental reporting might be affected by managed energy •u How an energy management program might evolve into a full workload and natural re-
source management program
Chart Your Course The rules of navigation never navigated a ship. The rules of architecture never built a house. Thomas Reid At some point, your program might become a full-fledged business unit with its own profit and loss statement. Or, as we recommended earlier, you might simply get energy management to a point where it is fully integrated into one or several operations. Where it goes depends on your leadership’s understanding of value, placement, and growth. We tried to avoid the theoretical in this book and to present a more how-to focus. However, it is important to have a basic trajectory planned out ahead of time. This helps to avoid surprises while building morale within your team because it demonstrates our oh-so-human need for progress. In our case, we had deep passion for technology, architecture, and the environment. We had both decided independently that applying our technical knowledge to some greater good was important. John’s experience with utilities and software engineering, along with mine in environmental sciences and facilities, pushed us toward energy management. From energy, we saw distinct similarities across a wide range of natural resource management applications. Water is the most obvious, but we also considered the more complex but much needed ones such as the source, transfer, and fate of pollutants. With all this as background, we cannot provide a guide
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| Chapter 10 Preparing for the Next Big Thing on passion, but we can share our thoughts on where IP enablement for energy, water, and pollution management could be heading. Our goal for the program we’ve been building at Cisco is to apply information technologies in support of improved resource management. In particular, we are interested in better managing finite natural resources. “Wall-outlet” electricity is, of course, man-made, but the majority of it does come from petroleum sources (coal), and burning it has a significant adverse environmental impact. Understanding the correlation between asset energy usage and the raw materials that are used to generate the energy is the first step in making the connection between IT operations and natural resource management. The next step is to see how technologies like virtualization, energy management, building efficiencies, and so on can ultimately reduce or make more efficient the consumption of these natural resources. In that regard, we would argue that managing or at least reporting energy usage is an extension of natural resource management. GHG management, water management, and pollution management are all simply extensions of the same resource management framework from our technologist perspective.
How We Did It: Cisco Green Task Force Our journey to a more robust focus on energy management technologies started in 2006. At that point, Cisco CEO John Chambers committed that the company would reduce emissions related to air travel by 10%, largely through the use of Cisco’s TelePresence technology. An executive “EcoBoard” was formed with the task of setting Cisco’s environmental strategy and overseeing companywide environmental programs, including assessing how networking technology could help the world better manage its energy and environmental challenges. Led initially by Laura Ipsen, Tony Bates, and Ron Ricci, the EcoBoard oversaw a larger multidisciplinary Green Task Force and included an early focus on a systemic approach to infusing “green” into the engineering organization. During this time, the teams were able to come up with a clear list of requirements for engineering, facilities, and IT operations around which to focus efforts. Through this process, it was clear to us that no one team in a large organization like ours could tackle something as big as Green all on its own. Executive leadership, cross-functional participation, regular communications, clear metrics, and achievable goals were all integral to our success.
Emerging Technologies Green technologies, although still loosely defined, generally span efforts to reduce, reuse, or recycle a physical product. These efforts include a reduction in materials and energy used to produce something or used by a product to perform functions after it is deployed. They also include reusing products from the developed world in lesser-developed countries or vendors taking back products and refurbishing them and recycling, which is well understood. These are the classic notions. An active energy management program is different in that it is focused only on the efficiency of an electrical service. To that end, you can walk a very neutral line. Efficiency management need not delve into the realm of making judgments of how an energy service is used. You are simply making energy usage and if preferable GHGs visible with the support of those to whom you provide the service.
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If you ultimately structure your energy management program to be a subset of energy efficiency, you should be able to apply the same programmatic framework to other areas. Your team becomes a form of efficiency broker until you get your operation’s energy consumption at peak efficiency. From there, and if the business drivers support it, you might get into water management, physical materials, pollutants, and governmental regulations. In our case, we are interested in efficiency, and we see standardization as a means to that end. Standardizing the inputs to an efficiency management program is a technological challenge that will keep us busy for years to come. Equally challenging is developing the software management architectures and business cases for utility grids, water management, and eventually pollutants. From a business perspective, the opportunities for natural resource management are fairly predictable. It is a simple numbers game in that we track population growth and per capita consumption fairly well. Conversely, you will start to understand the costs of new capacity for electricity, water, and even food. Although this starts to get into civics management and behavioral economics, delving into the top 20 percent of these issues will help prepare you for the next big technology opportunity. One key difference between civic planning and business case scoping are the price-point drivers that could trigger an investment from a business. For example, you can look into your corporation’s water bill today and see how it has been trending. Project it out to a time when your finance team might be prepared to make an investment to bring the cost down. This is when you can move your efforts toward innovation from energy to water. Because you might already be paying for and wasting more water than you would like, this is certainly a parallel track that could be run in tandem with your energy management program.
Note If you’re interested in being an environmental technologist, be sure to round out your skill set. In addition to energy, look into water, food, hazardous materials, heavy metals, persistent organic pollutants (POPs), aquatic pollution, and the factors that in general go into the lifestyles we enjoy. If you can develop a sound, foundational understanding of environmental issues along with business acumen and technological know-how, you will be in demand for years to come. Natural resource management can only grow in demand along with the growth of populations.
Water Management Think of water management as a system that functions by using the same basic technologies as a traditional building management system. SCADA is the predominant protocol, and very little IP enablement is apparent. Furthermore, SCADA systems are often semi-custom designed for a particular building, plant, or utility subsystem and built on top of a proprietary vendor platform. Although all of them are built using legacy transport protocols such as RS-485, Modbus, and LonWorks, most SCADA systems offer a web access option. This web access will, at the very least, enable you to monitor building information such as electrical and water usage and temperatures. As with buildings, few primary drivers exist to encourage the water management and treatment industry to adopt Internet protocols. This is primarily because, for most of the developed world, water is still plentiful and cheap. Does this sound like energy management today to you? Finally, water and electrical consumption are inextricably linked in the way we supply them. We use water to generate electricity in every form, and in the United States, generating electricity is the number one source of water usage. Conversely, we use a significant portion of electricity to move water. It is easy to forget that we’ve had pumps running full-time for many years now. A good example is New York City, where the subway tunnels would fill with water within 48–72 hours of no electrical supply. Water management and energy management will be
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| Chapter 10 Preparing for the Next Big Thing uniquely positioned to benefit from IP enablement in the coming years and give us a fighting chance to slow consumption as new sustainability models are created. As with energy, there are many types of water management. This section focuses on measuring, monitoring, and managing water supply by using an IP-enabled approach. There is some precedence here, as protocol converters with a web-based front end are on the market today. Some software packages also can give basic dashboard capabilities for water management. Table 10.1 provides an example of some of the monitoring and management components available today. Many components that support water management on the market today are not natively IP-addressable. This is changing slowly but surely. After you have your energy management program up and running as both IT and facilities assets, you should be well positioned to extend your management frameworks into water. By bringing facilities assets (UPS, CRAC, PDUs, and so forth) onto the IP network, you will be developing expertise in mediating SCADA and IP networks. This expertise will enable you to start bringing water management components onto an IP backbone. After you start mediating these components, along with purchasing new components that do report natively via IP, you can begin to make water use visible to your stakeholders. In summary, it is easy to add a field for water today to your energy management dashboard. You can either get this as a roll-up from a SCADA system or from IP-enabled water meters as they become available.
The Tower of Babel The heart of the challenge in better managing our energy and water availability and security really comes down to standardization, modularity, and communications. Although you can transport and translate building data to an IP-based network, the system components are not yet reporting natively via IP in most cases. This creates challenges in the accuracy of the data and increases the administrative costs to manage these components and the services they provide. These language and design issues are not confined to energy and water management, but are relevant across the facilities and IT industries. One of the best analogies I’ve heard to describe the issues at play between IT architectures and missioncritical facilities design was from APC’s founder, Neil Rasmussen. He was addressing the challenges related to data center power, cooling, and space that his company planned to overcome for its clients. Prior to APC’s release of its InfraStruXure (ISX) architecture in 2001, few modular, scalable, and targeted availability options were available in smaller power and cooling system increments. Neil was describing the significance of the issues that this ISX approach addressed and dropped a reference to the Tower of Babel. The story was about ancient Babylon, where people sought to build a tower to touch the heavens, but rather than build it for noble, holy reasons, decided to build it for man’s own glory. Upon learning of this, the Hebrew God Yahweh scattered the languages of man and cast the people across the lands. Prior to this transgression and punishment, man spoke a single language and had a single intent. The lesson I took from this analogy is that if there is too much focus on building the best point solution and not enough time spent on ensuring that the solution delivers broader, cross-platform value, you have a recipe for disaster. In the case of our industry, God is, well, your stakeholders, and the disasters are the threats to your business availability and sustainability. In the broader context of climate change, the same analogy applies, but the stakes are much larger. All nations will need to work toward a common end, or the gains made in some regions will be nullified by losses in others.
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Table 10.1:
Instrumentation points for water monitoring and management today are rarely IP-enabled
Component
Function
Native Protocol
Analyzers and process controllers
Analyzers are instrumentation points used to detect and report on ammonia, nitrite, phosphate, aluminum, and calcium in brine, chlorine, chromate, copper, hardness, iron, manganese, silicate, and sulfide. Process controllers provide predetermined controls to defined system functions across a wide range of watermanagement efforts including pump controls.
SCADA
Remote telemetry units (RTUs)
Using RTUs, telemetry systems interface with distributed control systems for water management including water quality, distribution, and stream gauging.
SCADA with some webbased (HTML) options
Motor controllers (MCs)
MCs work with motor control centers (MCCs) to regulate the actions of electric motors. These include electric motors for pump systems, valves, flood gates, storm drains, and so on.
SCADA
Meters
Similar to a power meter, there are many components from which you can choose that measure a capacity consumption rate (flow rate) for water. Many meters deployed today have no networking capabilities at all.
SCADA with a large installed base of nonnetworkable units
Heating, ventilation, and air conditioning (HVAC) components
There are a wide variety of components within a building related to precision and comfort cooling. These range from basic thermostats to specialized data center temperature-sensor networks.
SCADA (Modbus is predominant) with some web-based options
Portable samplers
These are primarily used for site waterquality testing. New units are coming to market that can be temporarily installed and measure water quality, flow, temperature, and telemetry.
SCADA and IP (primarily wireless cellular and 802.1X)
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| Chapter 10 Preparing for the Next Big Thing The application of improved water management that relies on Internet protocols is dependent on the use case. Figure 10.1 shows how water is used in the United States. It might be surprising to know that we use more water to generate thermoelectric (coal, nuclear, natural gas, etc) power than for any other activity, even agriculture! These use cases all rely on comparatively antiquated management technologies. Bringing these systems and technologies into the IP world will provide new savings opportunities and thus new funding sources for your program.
Figure 10.1 The U.S. Geological Survey’s estimated use of water in the United States in 2005
Public Supply Domestic Irrigation Livestock Aquaculture Industrial Mining Thermoelectric
Regarding the need for new solutions in this space, all signs point to increased demand for water. Increasing populations coupled with dwindling snowcaps will surely strain future water supplies. The April 2010 issue of National Geographic focuses on water, and it notes these interesting facts: •u 2.5 percent of the Earth’s water is fresh, and two-thirds of it is currently frozen. •u 46 percent of the world’s population does not have water piped to their homes. •u Within 15 years, 1.8 billion people will live in regions of severe water scarcity.
You can already tie in water management components as just another facilities entity in your energy management platform. However, the type of native IP-reporting approaches implemented in systems such as Cisco EnergyWise are a long way off. After all, EnergyWise is simply leveraging the operating system information directly from an IT asset. This level of computing functionality might not be available in water-monitoring devices. Therefore, instrumentation and management software must be developed in tandem, and it is something to which we will be turning our attention in the near future. One of the founders of EnergyWise, Matt Laherty, is already working on these management architectures. Figure 10.2 shows an example of how you can access water data over an IP network today.
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Figure 10.2 Water-monitoring and control devices can be accessed through building mediation technologies today.
User Interface
Internet Public Clouds User Interface
Web Servers
Data Center Private Clouds
BMS Mediator Building Management System Remote Terminal Unit Water Sensors and Controllers Building Water Supply
We discussed with Matt Laherty some of the ways IT technologies are being applied today. We mentioned earlier that Matt is looking into this in his role as a consulting engineer in Cisco’s office of the chief technology officer, Padmasree Warrior. He summarized what he is seeing as of July 2010 as follows: •u IP enablement can provide users with a real-time view of water consumption. This tells
them how much they’re using and can provide proactive altering when pressure drops (because of leaks) and when certain thresholds are exceeded. •u Water utilities can alert customers in new ways (email, text messaging, and instant mes-
saging) to warn of system stresses or faults. These alerts could result in a quick response to a conservation need. •u Pumping efficiency can be improved by alerting systems operators of their impacts on
electrical and water demands. •u IT systems can be coupled with “intelligent” gates and pumps that enable combined
waste/storm sewers to store runoff to avoid combined sewer overflows (CSOs) and overrun sewage treatment plants (STPs). •u IP-enablement technologies coming from smart grid investment will likely be applied to
water management. In speaking with Matt and doing our own research, it was clear that water management is emerging as an area that needs more attention. At the time of this writing, we did see the same business drivers as we saw with energy management, but in the larger context, we can survive without energy. As water stresses become more acute in a warming climate, so too will the business drivers and in turn investment in new technologies. We have a long way to go when you think about it. Is your water utility sending you a text message on water-quality thresholds yet? They could do this and for a lot less money than most would think.
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| Chapter 10 Preparing for the Next Big Thing Cloud Computing and Energy Management With the advent of computing and storage virtualization technologies comes a new paradigm. A decoupling of hardware and operating systems is moving the IT industry toward more-flexible service-oriented architectures. As this evolves, the need for energy and physical information will become more and more important for cloud administrators of all kinds. As the social awareness of Green evolves, a user’s interest in the impact of his or her own activity will also increase. Technically speaking, an enterprise-level public or private cloud will have many challenges to address. Energy is only one of these challenges, but it is the most critical in terms of a cloud’s availability. Not only are there risks for uptime due to regional power-quality differences, but there is also significant cost exposure. There is a high degree of variance in electrical costs globally and, in some regions, large swings in costs throughout a given day. If a cloud administrator is moving load without knowing these costs, exposing the corporation to higher energy costs becomes a real potential. As you move past this book and begin to extend your IP-based energy domain constructs into facilities, keep the cloud in mind. Physical telemetry for assets and real estate will be critically important. Subsequent levels of your program development will likely need to address cloud architectures and facilities asset integration in tandem. Doing both will be required if you want to provide cloud energy management services to your stakeholders. If you’re a global corporation, at the very least you will need to incorporate regionally specific electrical costs, temperatures, and capacities in application provisioning and in decisions about virtual machine mobility. The rest of this section will take you through some likely scenarios and steps you can take to address these decisions.
Virtual Machine Motion Moving computing loads and the electrical loads that follow at the scale of multiple kilowatts in a single data center facility can cause major problems, though not the obvious ones, related to potentially overloading a circuit. We all know what happens if you try to pull too much energy. The problem we’re referring to is the opposite case, in fact. We first witnessed genuine concern about this when computing virtualization was just starting to be discussed with its current fervor around 2005. We were in a meeting with Paul Marcoux, who at the time was the head of APC’s Science Center and who has managed, designed, and built just about every aspect of data center facilities. Paul looked us square in the eye and said, “So you’re telling me that this server virtualization technology can introduce as much as a 20 percent variance in server computing load across a data center?” We responded, “Well that’s what the marketing says, so it’s referring to a future state.” His answer was one that was telling in many ways: “I don’t know of a data center facilities manager in the world who would sign off on that without designing a site specifically to accommodate it.” The reasoning behind this is that data center UPS and air conditioning are designed not just for a high-end utilization but also for the low end—that is, a window. “When you start getting below the operative window of power and cooling systems in the three-phase world, things get wobbly,” Paul said. Finally, he added, “You also run those power systems inefficiently at lower loads.”
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This scenario again points to the need to work closely with your facilities professionals or, better yet, hire a few into your program. The need to understand power and cooling profiles that account for how power changes over time and by what triggers will be critical as cloud becomes the norm. The good news is you can design a data center today (physically) to accommodate these large load variances. The key here is the ability to design data center facilities into highly modular zones with different operative profiles but within the same site. Prior to the advent of more-conducive facilities modularity around 2003, this was very difficult to do. Now you can more easily target your facilities’ design to accommodate high-density, highefficiency IP manageability and in smaller power increments than previously required. These smaller increments enable you to parse out mobile computing loads within defined parameters. This does not change the fact that power support systems run better with relatively static loads, but it does mitigate the risk of failures by having multiple increments for failover. Several approaches already exist for tying in energy data to VM provisioning—including those from VMware, Sentilla, Power Assure, and formerly Cassatt (now part of Computer Associates). Some also allow for temperature and telemetry inputs. However, all use a primarily middleware approach that relies on third-party inputs for data on power, cooling, and location. The discovery of common monitoring and common controls (policy engines) across infrastructure platforms is where we’ve spent most of our time. As these approaches come together, what should ensue in the next few years are fully automated management applications that address power and cooling related to VMs. Until then, this is something that needs to be managed inhouse with current tools. Although they are not perfect, some best practices do exist that can help you mitigate risks related to dynamic electrical loads resulting from cloud-centric operations: Adopt Zoning Our working definition of a data center zone is a physical construct that is built in support of a logical architecture. With the advent of 10–80 kW UPS systems that can hot-scale, you can target power capacities in much smaller increments than ever before. Scalable UPS systems coupled with self-contained, high-density cooling systems enable cloud managers to build highly standardized, preengineered increments. These increments, or zones, can be built to a specification that is rationalized against particular criteria such as dynamic electrical loads/virtualization, high efficiency, high density, and so on. Zoning is a planning and operations organizing principle, and it lends itself very well to the future implications of cloud architectures. Associate Energy Management with VM Provisioning At a minimum, one or several of the options we’ve discussed in this book should be adopted for monitoring. With basic monitoring in place, it is a fairly simple exercise to write a script against defined power-capacity thresholds. This scripting can be integrated into many of the VM provisioning applications already available. The governance this relationship can provide is a “do not provision in zone 1 if capacity required x is greater than capacity available y” type of quality check. The definitions of capacity will be use-case specific, but this simple hack is already being used in many data centers today. On the other hand, you should look into spinning or shutting down assets after a VM load is moved to another zone because many legacy server platforms will consume 30 to 50 percent of their total energy requirement while idle. Deploy Temperature Sensor Networks A temperature sensor network (TSN) is simple and inexpensive to install via IP. Several vendors provide temperature, humidity, and barometric probes that connect via RJ45. These probes can be tied into many applications including Cisco EnergyWise. They typically hang off the end of a patch cable, and two can be deployed
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| Chapter 10 Preparing for the Next Big Thing per rack. One is placed high, and one is placed low, but both are on the inlet side of the IT infrastructure. These temperature inputs coupled with the inlet return temperature of a CRAC unit will paint a picture indicative of a site’s cooling system. The same energy scripting foundation covered in the preceding section applies here except the capacities are thermal, not electrical. This quality check will help you manage what is sure to be a common problem with cloud infrastructure architectures—that is, mobile hot spots. Build Associations In our case, our Network Data Center Services (NDCS) team does a great job of tracking a wide range of resources. From VMs to CPU capacities to LUNs, a lot of information is associated with our energy data. These associations provide a wide range of predictive-modeling options. These associations, such as watts per rack to watts per VM, give us foundational data sets to develop our own meaningful efficiency metrics. At another level down, we are working to associate typical IT metrics with facilities metrics such as watts for cooling and watts per floor per building. The need for these associations was a determining factor in our strategy to integrate our energy management efforts into other programs.
Follow-the-Priority Computing With the advent of VMotion and vBlocks, it is fully conceivable that data center operators will be able to move lab and data center workloads all over the globe. This has massive implications for how Green (or not) your operation will be. It also has content-specific legal implications. As you can imagine, highly mobile computing loads will give an operator the ability to choose the type of power (by region) that supports IT workload requirements. There has already been a lot of blogosphere discussion on the implications of computing (VM) mobility. If this area is of interest, you can simply use Google to search follow the sun and chase the wind computing, follow the moon computing, and follow the law computing. One of our colleagues at Cisco, James Urquhart, has blogged about some of these implications. Another cloud energy implication relates to the “eco-ethics” of delivering services as a cloud provider. To frame this, think of data centers as sneaker factories. If in the future the type of power you use is visible to all, could that threaten the standing of a company’s brand? If you choose to locate your data center in China, plugged into cheap, reliable coal, is that analogous to running a sweatshop in Indonesia? This might seem laughable under the current climate, but as pressure mounts on corporations to be more and more Green, this situation could be plausible.
Data Center Energy Management Data centers, like Green, are a loosely defined entity. Both IDC and Gartner have offered taxonomic descriptions of a data center that go into more detail than covered here. We keep our working definition of a data center simple and somewhat nonquantitative: A data center is a purpose-built room or facility that houses a business’s mission-critical applications. A Green data center effort is one where specific planning, design, and/or operations have been implemented to use energy more efficiently, source cleaner energy, limit physical materials, source sustainable materials, and account for building-specific site impacts. These environments are far and away the most complex ones in IT today. We warned in earlier chapters not to jump too far ahead in your efforts to manage energy and risk a data center’s availability. Given the trend toward cloud architectures, a shift should occur in energy
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allocations from office space to data centers. As this shift takes place, your program will need to scale to support energy management in these critical environments. Centrally administered, cross-domain data center energy management might be out of scope today, but it is very much on the horizon. The next few sections will take you through some key technologies to explore.
How We Did It: Benchmarking Data Long before we discussed a specific environment that we wanted to manage (lab, office space, or data center), we focused on the benchmarking data. We found that many in the industry were discussing data centers at great length without discussing what the total savings opportunities in data centers were in relation to the total energy requirements of the enterprise. We found at Cisco and for most of our clients that data centers are high in power density requirements but low in total power requirements when compared to the broader real estate portfolio. Companies running MSDCs are the obvious exception but for most companies we’ve worked with, data centers are in the minority of total power allocations across the enterprise. In addition, data centers are also intended to be mission-critical facilities. In our case, we have scoped energy management for production data centers to include monitoring only. At Cisco, we are heavily embracing virtualization for both computing and storage, and we are seeking to integrate energy management in a cloud model. Until we can manage energy at the information-stream level, we are focusing on monitoring and limited, domain-specific data center energy management.
3D Geospatial Asset Inventory Management In the last few years, major advances have occurred related to asset management in data center environments. Many of these tools provide asset management capabilities through three-dimensional user interfaces. These interfaces rely on an initial rendering of assets into racks, and some of these applications can actually automate the rendering. We’ve evaluated Aperture from Emerson Network Power and Rackwise. Both were very compelling in the level of detail they provide on the physical and operative profiles of assets. At the time we evaluated these systems, they could not perform CFD modeling. (We use Futures Facilities 6Sigma for CFD.) What was clear among all three applications is that they should become closer over time. Our interest in applying these tools relates to having active electrical and thermal feeds displayed within an accurate physical representation of a data center. These environmental data points could then be overlaid or compared against other feeds coming in—for example, latency, CPU cycles, VMs, and so forth. Our data center clients today are interested in managing assets more effectively with tools that are relatively simple to use. One of the challenges we found in using the current tool sets is that they are highly specialized and require significant training to use effectively. We have seen, however, a trend in these applications over the past three years toward automation and simplification. If these trends continue, you should consider these types of tools as part of your strategy to expand your program into managing multiple resources.
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| Chapter 10 Preparing for the Next Big Thing Temperature Management Depending on whom you ask and what they are selling, most data center efficiency experts will tell you to start with cooling. It is often the “low-hanging fruit” in a data center or lab operation. The traditional practice has been to overcool, pushing air under a raised floor along with cabling and leaving the economizer function for air conditioning units disabled. This results in a situation of low-cooling efficiencies. One of the best ways to get a handle on your data center’s cooling is to deploy a TSN. These simple networks can be wired or wireless and, when deployed properly, give you a good thermal map for an environment. This map can then be assessed against the manufacturer’s recommended cooling requirement of deployed assets. What you will often find is that you are overcooling far beyond this published requirement. For example, if we say a Cisco Unified Computing chassis requires an input temperature of 95° Fahrenheit and your sensor network is measuring 82°F, you are overcooling. In addition to helping you identify areas of cooling efficiency, a TSN can also help you track thermals as you increase your set temperatures. In the preceding example, we would not recommend turning the set temperature of the cooling system up to 95°F overnight. Rather, you should raise temperatures in increments of 2–3 degrees and observe the impacts on the overall cooling system. Cooling systems can be fickle with big swings in temperature, so proceed cautiously. We assessed SynapSense’s pre-engineered solution for temperature management in data centers and were impressed. Conversely, our Cisco IT teams built their own, lower-cost system that simply had temperature probes hanging at the end of a patch cable. Both gave us key data sets that we think in time can be integrated into resource management interfaces. It is likely in the coming years that cooling management will be included along with energy management. For this reason, becoming familiar with these monitoring systems will add to the value your program can deliver.
Efficiency Modeling There is still much debate over what efficiency even means in the context of a data center. Where possible, we try to develop simple key performance indicators (KPIs) that can be applied to specific workloads. For example, we have used watts per port for years but also look at watts per CPU, watts per VM, and watts per LUN. These give us a way to split out allocations by existing infrastructure management functions. The highest-level metric we look for in data center efficiency is simply a percentage of total power that we define as wasted or not currently productive. We have limited active control over some of these areas, such as power-conversion losses in switched-mode power supplies. We can control others, such as power used while an asset is idle. To frame this high-level efficiency metric, think of it as an automobile’s fuel consumption to keep the engine idling but not driving anywhere. The way we run our assets today would be like us leaving our cars idling all the time, whether we are using them or not. That wasted energy is our focus, and we represent it as a percentage of total energy consumed. Any energy you save in the critical IT load has a cascade effect into the larger facility. Emerson Network Power has published a good white paper on this, which can be found by using Google to search Emerson Energy Logic. Because of this situation, we chose to start our efficiency modeling by looking at IT assets first and facilities assets second. To that end, we worked with Cisco’s Internet Business Solutions Group (IBSG) to build the Green Data Center Model
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Calculator. This tool is publicly available at www.cisco.com/go/efficiency. It focuses on the electrical and procurement savings related to computing and storage virtualization. We created a second tool that focuses on product-level efficiency. We decided that, as a vendor, we needed to provide data on how to design our product deployments efficiently. Although it is difficult to make changes to provisioned power supplies after they are installed, it is not difficult initially to properly size the power requirements. To that end, we developed the Cisco Product Efficiency Calculator, which can also be found at the Efficiency Assurance Program site (at the preceding URL). With the publication of this calculator, Cisco became the first vendor, to our knowledge, to publish power-supply efficiency curves publicly. These efficiency curves show our clients where they need to load a product to achieve its highest operative efficiency. In general, we see a need for improved efficiency modeling in data center environments. Among 3D asset management, CFD modeling, TSNs, and online calculators, many options are available today. What we haven’t seen yet is something that brings them all together in a meaningful, yet simple, way. As our program matures, we will have a greater need for modular software management tools to bring it all together. Today we use Google SketchUp with much success to develop what we call collaborative infrastructure models (CIMs). SketchUp gives us a simple, extensible tool to model the infrastructure, and when needed, we’ll employ 6Sigma for more-detailed modeling. You will eventually need a way to model not just efficiency, but the consolidation opportunities you uncover through energy monitoring.
On the Horizon A goal is not always meant to be reached; it often serves simply as something to aim at. Lee Jun Fan (Bruce Lee) 李振藩 A key area we often neglect as technophiles is the marketing side of the business. Marketing and technical disciplines like IP and building networks are not always natively conversant. However, if you are a technical person and don’t know this discipline, you should in-source some talent. The right analysis of and communications to your stakeholder groups are critically important to your program’s longevity. Furthermore, a strong marketing manager can help you aggregate the passion and commitment of your employee base. Your broader employee base will be a valuable source of input as to where help is needed. In earlier chapters, we covered the caution we used when approaching energy data. That holds true as your program grows. However, you might have the opportunity to expose cost savings and reduction in CO2 emissions to a broader audience at key milestones. This is not just your chance to shine, but more important, it is your opportunity to showcase the efforts of the teams to whom you are providing the service. Take your time when considering how to encourage grassroots participation, and work with a marketing professional to deliver your updates in a contextual and meaningful way. As you begin to communicate your results to broader audiences, it is important to provide a dialogue with them. If you settle for a one-way, group email, you are missing an opportunity to broaden your resource pool. We established a blog with our clients and employees that enabled us to leverage the time and expertise of passionate individuals. Capturing these contributions by using a shared space and directing them toward common goals was well
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| Chapter 10 Preparing for the Next Big Thing worth the up-front marketing efforts. The rest of this section describes some possible scenarios as you build out your program.
Making Energy Usage Visible Although it is hard to predict just how much savings will occur just be making energy usage visible to the consumer. Today the vast majority of your peers are completely ignorant about the amount of energy they use. In general, we’ve found that people want to do the right thing, but in today’s fast-paced world, we recognize the need to make it easy for them if we are to expect results. Providing you can do this, you can expect an increase in the awareness and, therefore, the action related to wasted energy. It’s like showing people the lights are on in their house. If the people can see the lights are on and can reach the light switch, they will turn them off. As we mentioned in earlier chapters, energy information is sensitive, and you should never expose it without stakeholder consent. Providing you have that consent, you can do a lot with this information. The obvious Green analogies apply (cars off the road, trees planted, and so on), but you can also show the amount of energy used per user. Total capacity used per user is not necessarily a true measurement of how Green someone is, but efficiency of their energy use is such a measurement. As you decide how to make energy use visible to the larger organization, you will uncover innovative ways to provide incentives for moreefficient energy use. After IP-enabled energy management progresses to a point where you can tie wattage to information streams, behaviors might change significantly. Imagine a point roughly five years out from today, when standards drive vendors to report the “in-chassis” energy use of particular IT assets. This reporting is tied to the energy used to perform a particular function (for example, serving data), and it is collected through multiple points of network aggregation, transport, and storage. This energy usage data could then be tagged, maintained, aggregated and exposed across the life cycle of an information stream. In this scenario, search engine providers, social media networks, and corporations could show their consumers the energy use (and CO2 to which it correlates) of a particular cloud service in close to real time. As an example, you could have a Mozilla extension or Google widget adding “cars on the road” icons to correlate to your web surfing. This would drive a whole new level of information to aid consumers in “voting with their wallets” for the least carbon intensive web services.
Power to the People As you conquer the technical barriers of exposing energy information and providing some automated controls, you will soon have a new challenge. The human behaviors we have acquired for energy usage in the past 100 years will not change overnight. You will need to be creative in the way you provide incentives for change while limiting disruption. With this in mind, it might make sense to invest in some internal marketing resources. As information is made available to more and more stakeholders, more contexts will be needed. To facilitate this, a modest company intranet page can be set up. This can have a few simple elements that will appeal to multiple levels of stakeholders across your company. A simple information architecture can be translated into a website with the employee resources shown in Table 10.2.
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Table 10.2:
Intranet reference sites can help a program scale
Content
Delivery
Education materials on energy, water, and how the ecologic can complement the economic
Video on demand (VoD) systems are an ideal vehicle to impart key concepts quickly. Work to keep VoDs to 5 minutes or less as a best practice.
Case studies that highlight employees who have made a positive impact through participation in your program
Downloadable text documents are fine, but, where possible, shoot a VoD interview with individuals who are contributing.
Technical resources that cover the key attributes of your energy management architecture
White papers, data sheets, and solution overviews are all of value. Podcasts that narrate technical points in PowerPoint form are also helpful.
Planning tools, power, cooling, and water calculators that enable efficiency analysis
You can use online interactive calculators that are tied to your company’s business processes. These can be tied into management applications and placed at the beginning of an adoption process.
Industry associations and Green partnerships by organization
Provide web content covering what your corporation is doing publicly to be Greener along with links for further learning on external sites. Any joint partnerships can also be highlighted here.
Leader boards showing any Green competitions planned or underway
Provide web content with live feeds from managed environments that show how labs, data centers, and buildings rank against each other. Be sure to include an efficiency metric and to not be solely focused on energy capacities.
Interactive spaces such as personal web pages, blogs, videos, and file sharing
Embrace blogging and build a space that will attract people through rich (but not frivolous) interactive media. Include personalized pages for team members with lists of accomplishments, Green tips, favorite Green readings, and so on.
Using External Programs An energy management program will eventually get your operations to peak efficiency. Whatever you set as your efficiency target, you will eventually hit it. When you do, the capacity for your team to grow might be limited. When this point is on the horizon, you may want to look to external partnerships to grow. If your corporation has a government affairs function, it makes sense to partner with them. As energy management becomes more of a mainstream IT service, there should be an interest by regulatory bodies to access this information and automate its aggregation. It is hard to
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| Chapter 10 Preparing for the Next Big Thing predict what governmental incentives will be in place for energy in the future. If you have a contact in your organization who can help you stay at the forefront of developments in this space, partnering with that person might provide an early insight into new sources of funding. These might be straight grants or could come in the form of frameworks for regulating emissions.
How We Did It: Tracking Facilities and IT Data At the time of this writing, we are establishing an internal site that tracks a range of data about facilities and IT assets. Electrical consumption, temperatures, virtual machines, and physical locations are now all being integrated. Using this data, we plan to run internal competitions among lab managers to have the Greenest lab in the operation. Selections will be made quarterly, and the plan is to provide prizes and incentives for labs achieving the largest savings. We’ve run similar contests, and we have been amazed by what people will do for an iPhone. This competition will enable us to capture the significant passion for Green in our company outside the 52-member virtual team that made up our initial Energy Management Tiger Team.
Energy-Efficiency Incentives As electrical and water capacities are strained, utility rebate programs could become more prevalent. The first question you might ask yourself is, Why would an energy utility encourage users to be more efficient with the effect of reducing total energy use and thus its revenue source? It would be analogous to a gas station encouraging you to use less gas. However, this is the case with many utilities across the globe. I have discussed utility incentive programs with Mark Bramfitt, a consultant to the utilities and IT industry and former Pacific Gas & Electric (PG&E) principal program manager, on several occasions. To paraphrase what I’ve heard from Mark and others, utilities that offer rebate programs are not trying to put themselves out of business, but are making an effort to deliver a reliable level of service with semi-fixed capacity constraints. In essence, these forward-thinking utilities are looking at their business models with sustainability in mind. Most recently we saw PG&E deliver a $1.43 million rebate to NetApp for an energy-efficiency initiative they implemented: As a result of NetApp’s energy-efficiency improvements, its data center is projected to operate at power usage effectiveness (PUE) of less than 1.3, which is considered a best-in-class metric for data center energy efficiency. In addition, PG&E estimates that NetApp will save more than 11,100,000 kilowatt-hours each year, which represents a savings of more than $1,178,000 and a reduction of carbon dioxide emissions by 3,391 tons annually. In addition to rebates from utilities, you also see some passive-reduction strategy incentives, such as the Leadership in Energy and Environmental Design (LEED) certification from the United States Green Building Council (USGBC). These point-based rating systems look at measures that can be taken in materials and energy considerations for new construction and retrofits. Although we haven’t discussed passive strategies much in this book, seeking a LEED certification should absolutely be within your scope of considerations if you’re involved in building a new site. Many states in the United States already offer tax incentives for LEED certifications.
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Resource Management If your program is scaling successfully, you should see multiple opportunities to integrate energy information. Integrated dashboards covering the many resources that are provisioned are particularly important in lab and data center environments. Roll-ups of this information are of value to infrastructure managers and to the new class of sustainability officers joining the workforce. Energy efficiency often lies in the balance of these resources with one another. Taking your program from a state of asset management to resource management requires establishing common linkages across pools of resources. Improving how you manage IT and real estate resources is by extension an upgrading of how your organization manages natural resources. This is where our Green passion is being applied. However, we are focusing on giving the tools and awareness to those who already manage facilities and IT. Although there is a drastic environmental need for improved efficiencies related to IT, we believe that if we were to push too hard, we would run the risk of not scaling a program—or even getting it off the ground. With that in mind, we limited the scope of discussion in this section to the types of data and methods that are relevant to your program. We do not suggest that your program should manage the resources we cover in a top-down manner. Historically, we have done a decent job of managing computing resources. Networks, driven by security, are for the most part well managed. Storage suffers from our human tendency to not clean up the garage, but it is improving with many larger enterprises adopting tiered storage strategies. All of these domains will have their own tools, maturity models, and protocols for managing resources of a particular type. In this section, we are focusing at a high level on the building blocks of a program that will help an organization better achieve energy efficiency. How you weave these blocks together to provide a managed “story” of your organization will be up to you.
Tiering As your program evolves, you will be in the position of making recommendations on what infrastructure to shut down and where consolidation opportunities exist. Before you can make a sound recommendation on what infrastructure to spin down, you will need to know the applications that infrastructure supports. We mentioned in earlier chapters that you will be seeing electrical “flat lines” after you deploy monitoring. We then made the recommendation to monitor infrastructure for a period of time to see whether that line ever changes. An additional quality check is to perform an application-profiling exercise to understand which applications might use these flat-lined assets. Some applications run in quarterly batches, and some infrastructure might be designed for disaster recovery purposes. You should seek to understand these dependencies before making recommendations on any shutdown for a production environment. The approach we’ve used is to align asset tiers to application tiers. These asset tiers can then be associated to an importance level for an energy domain. This can be subdivided all the way down to individual entities (assets) if needed. The same methodology can be applied in reverse. You can align the infrastructure and applications to data. This is where EMC’s Information Lifecycle Management (ILM) comes in. As the Mr. Green at EMC, Dick Sullivan, likes to say, Green IT starts with your information management strategy. Said another way, all the IT and much of the facilities infrastructure for your business are there to support what your company does with its information. Alignment of resources up and down a tiered structure is an area where your program can add value. However, today this requires a serious internal investment to fill the many gaps in resource management tools. Although there are enterprise management applications out there
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| Chapter 10 Preparing for the Next Big Thing today that can help, they are far bigger and more expensive than you will need in the early stages of your program. Our plan is to advocate for more functionality in existing applications while building out open instrumentation solutions from the bottom up. If your organization is already investing in this space, you might look to integrate energy early on and see how you can scale through an internal partnership.
Defining Workloads A lot of work is still required to get IT systems to a point of standardization enabling us to issue the equivalent of a miles per gallon (mpg) metric. This is not to say it can’t or shouldn’t be done—it can and it should. What we are saying is that the reality today is that IT is a hypercompetitive industry, and standardization of anything is a challenge. Something as ubiquitous as energy and how it is used by different components also carries with it a challenge of scope. Some argue that large IT vendors have a monopolistic agenda and want to corner the market through proprietary barriers. The antitrust press centered around Microsoft might have set a precedent some years ago. However, in our experience, we have seen an ethical and committed effort for working toward standardization related to energy management. In our case, we chose to keep the Cisco EnergyWise APIs open. Industry consortia such as The Green Grid and Climate Savers are moving all of us ahead on an energy-efficiency agenda. We are also working with many other standards bodies including the Internet Engineering Task Force (IETF), the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE), and the Association of Home Appliance Manufacturers (AHAM), and all are taking energy efficiency seriously. As industry standards are adopted, be sure to get involved with any groups that help you more accurately and effectively manage energy. In the absence of defined workloads balanced against units of energy, don’t wait—get started today with the working metrics outlined in this book and through some basic research that will enable you to learn as you go. Building a program from basic watts (time and capacity), cost (per watt hour), and allocation (who is paying for it) will establish the foundation for more-complex metrics as they become adopted in the industry. As of this writing, we employed metrics that assumed a particular workload. There was no one-size-fits-all metric available from any standards body or industry consortia. There is no linked correlation today between data activity and power usage that is extensible across and within infrastructure platforms. Until a software-based correlation standard is developed, we will likely need to infer and approximate such metrics until algorithmic associations can be applied. You will, in some sense, be building this into your database-level data normalization. This is what we are using for our program’s resource dashboarding. This gives us the ability to analyze simple workload metrics such as CPU utilization, virtual machines, and LUN allocations all compared against watts and cost. Getting your program to that point will set it up for more-precise metrics in coming years.
Bringing Power to the Packet Carrying on from the preceding section and staying with software-based energy correlation to a particular data activity, we again are faced with scale. However, think of the scale here as a basic process line. First you start by identifying what it is that is being created—let’s say a packet. What creates it? Let’s say a server or an endpoint device. This packet is tagged with an energy meta-tag based on the energy it took the server or end device to create any number of packets.
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Next the packet is moved and meta-tagged again by a series of routers and switches. It bangs around dozens more points of creation and transmission before it is finally stored. Each time the packet gets touched, a new energy increment is added to what is now an information stream’s meta-tag. If we can build highly automated software-based instrumentation, aggregation, and reporting systems that function across computing, network, and storage, then the standards might follow. This is not to say we advocate for homegrown standards, but a replicable solution that provides real savings should not wait if it works for you. We look forward to continuing to contribute to the many standards bodies all working toward a more energy-efficient IT industry. To add value to that effort, we will continue to build out solutions that provide savings today.
The Bottom Line Identify and understand adjacent technologies, resources, and operations that might help your program add value. Continue to develop your understanding of core program deliverables while identifying new application areas, sources of funding, and talent to ensure that your program can scale. Master It Put aside dedicated time with your team’s more-strategic thinkers. Conduct sessions with a scribe to go through multiple levels of mapping across technology, people, processes, and finance. Aggregate the content from these planning and mapping sessions, and hold annual review sessions that set the strategic direction of your program. Assess the user behavior and reporting implications of your program both internally and externally. There are bound to be some surprises as your program scales. Where possible, try to anticipate the less-tangible impacts of your program. Employing scenario-modeling techniques can help mitigate the unintended consequences of a well-meaning program. Master It Look for team members (both virtual and core) who challenge your thinking. Foster open discussion and objective feedback with these members through semi-regular steering meetings. Conduct these sessions in advance of major decisions that might affect the strategic direction of your program. Become a leading energy manager in the industry today. As the market for Green solutions matures, many opportunities for growth and change will arise. You will want to stay at the forefront of this quickly evolving industry. Continually improving your skills across energy and related resources will ensure that you stay professionally differentiated. Master It Look for opportunities to participate in shared learning exchanges. Seek out peers in core and adjacent fields of management and solutions development. Commit to filling gaps you identify in your own knowledge base and skill set. Define what a career in energy management means to you, and set personal goals to become a leader in energy management services.
Appendices In this section you will find: u Appendix A: The Bottom Line u Appendix B: Links and Resources
Appendix A
The Bottom Line Each of The Bottom Line sections in the chapters suggest exercises to deepen skills and understanding. Sometimes there is only one possible solution, but often you are encouraged to use your skills and creativity to create something that builds on what you know and lets you explore one of many possible solutions.
Chapter 2: Benchmarking Convert units of energy into different metrics and across different assets. You will need to have a basic understanding of how energy units are quantified monetarily and then converted. Watts will provide you with electrical capacities over time. With capacity and time, you can simply add in costs in the form of kWh. After kWh is determined, you can easily convert into CO2e. This basic process will apply to both Facilities and IT assets and can be aligned against existing management constructs such as building energy. Master It Calculate the total power requirements in watts based on the data sheet or asset/inventory list power figures for a subset of your infrastructure. Take your wattage and apply a cost to it (kWh) by using your local energy information website. After costs are determined, convert kWh into CO2e by using sites such as the U.S. EPA’s eGRID resource page. For other countries, you may need to check with the local utility provider to determine a CO2e for your local fuel source. Solution Keep it simple initially and build a basic spreadsheet with pivot table capabilities. This spreadsheet will serve as the foundation for more-complex modeling, tracking, and reporting databases. Be sure to provide a summary worksheet that provides a roll-up of the most relevant data points across cost, time, and capacities. Practice and apply the basic principles behind energy efficiency, including electrical losses through distribution, conversion, storage, and use-case workloads. Although you don’t need to be an energy expert to implement energy management, you should work toward being one. Take the time to work through the equations that underpin energy management, and in doing so, you will learn the basic physics of electricity. Master It Commit to a schedule of self-study to include 20–30 hours per quarter. This is roughly 2 hours per week of committed focus. Certainly spend more hours as time allows, but this should be a nondisruptive time commitment. We highly recommend some of the basic course work from the Massachusetts Institute of Technology (MIT) that is available for free on iTunes U. If you want to dive deeper, APC’s Data Center University provides sound data center fundamentals that are broadly applicable. A lot of free courseware is out there, so dig in and learn more. As you get through your first two quarters of
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| Appendix A The Bottom Line self-study (providing you are a novice to begin with), we suggest digging into the publicly available content coming from consortia such as Climate Savers and The Green Grid. The content on these sites will help you to move past basic electrical efficiency and into how energy usage varies based on the type of work (workload) an IT system performs. Solution Again, a simple spreadsheet can work well to structure a professional development pedagogy. This pedagogy will provide a development plan not just for you, but for any new team members you bring on to the effort.
Chapter 3: Assessing Value Establish a system for your data that is conducive to modeling different value points. Data aggregation and modeling will be critical elements to developing your program. Whether you insert your data into another management construct or build a new one, data organization is crucial. Master It There is data on watts, cost, and billing periods in many locations across facilities, IT, and finance teams. How do you best structure this data for an initial assessment of the potential value your program will bring? Solution First aggregate the data through basic spreadsheets. Second, set up pivot tables to determine what infrastructure consumes what wattage. Third, review this model foundation with the stakeholders who provided the data. Finally, based on stakeholder feedback and your own expertise, you should build in any normalization formulas to account for major inaccuracies. Prioritize data valuation by business functions. Facilities, IT, and finance departments all have different interests to which you’ll need to cater. How you present the value of your program quantitatively and qualitatively will be important to getting your pilot funded. Master It All of the stakeholder groups you interact with will have some level of interest in the power, cooling, cost, and carbon data you mine. What are the priorities for each team related to these data sets? Solution Through your identification of and interaction with your stakeholders, gather relevant content (presentations, strategy documents, organizational incentives, and so on) on the goals for these teams. Review this content, and map your data sets in terms of relevance and qualitative value. Set a goal for your team to make qualitative valuations measurable and include them in your pilot if possible. Model your data to build a modular value framework. Because you will be interacting with multiple stakeholder groups, there are multiple insertion points for the value your service brings. You will need modular data models that can be shared with multiple teams to garner support. Master It With business functions and organization incentives understood, you need to start mapping your value to the function’s value. How can you demonstrate the many points of value your program can bring in a context that is familiar to your stakeholders? Solution Break out your stakeholder programs into strategic, tactical, and executable overviews. After an executable plan is built, review it with your core team (this might be
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just you at this point) and ask yourselves, Can we provide visibility, normalization, and control for each point of the existing programs? For example, one point might be that the company’s total electrical consumption has gone up but the GHG remains flat. In this scenario, a real estate department might not realize that electrical use is up because travel restrictions have resulted in more people being in the office. Finance team members (if they are tracking GHG) might see that GHG is flat because there are fewer emissions resulting from air travel. Continue this priority mapping throughout the formation of your program. Ming mapping will work well for organizing your value matrix, which can in turn be put into presentation format.
Chapter 4: Managing Your Project Scope a proof-of-concept deployment and assess the core technologies that will support your pilot. You will need to have a basic understanding of how to scope a project in terms of the technology you will use and the skills you will need to administer it. Master It Map one to three technology solutions to each set of benchmark data as having a potential to reduce wasted watts. Based on the published savings for each technology, develop a savings target for a suite of solutions built within and across the different technology options. Solution Take the estimates of savings by technology application area (lab, office space, and data center), and map them to the applicable infrastructure architectures. This will include buildings, facilities, and IT assets. Find the individuals within IT and facilities departments that support these assets, and ask for their assistance in assessing energy management technologies as part of a proof of concept. Finally, assess what contributions these individuals could make as part of a virtual team and scope all of the preceding elements in terms of level of effort. Effectively manage collaboration. You will need to set up and maintain a collaboration space for your project team to aggregate and track content and progress. Master It If you are already using a good suite of collaboration tools internally, it probably makes sense to stick with them. Using a tool that is already familiar to your teams is ideal. Solution If no simple, effective collaboration tools exist or you want to complement the current tool set, start up a Google Group or the equivalent and expand it. There are many Web 2.0 tools out there that are completely free and can help you fill gaps as needed. Establish and maintain regular communications. You will need to establish a communications plan to keep your project team and stakeholder groups informed and working toward common goals. Master It Look around the organization to see whether there are any marketing resources that can contribute to your efforts. These teams can take your raw content and polish it to the point that it is clear, concise, and professional in appearance. Solution If no marketing resources are forthcoming, use any existing templates that are available for internal communications. Just like collaboration and project management tools, there are many email and newsletter templates that can be found on the Web. Get the
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| Appendix A The Bottom Line templates and start practicing until you find a template and look that works for you. In our case, we were Mac users, and many of the native OS X applications enable you to be a oneperson marketing machine. As an example, you can visit www.youtube.com/greencisco.
Chapter 5: Building a Pilot Deployment Select an engineering team for your pilot program. When you start on your pilot, you’ll have to select a small engineering team that can start the program and then grow it into a production rollout. Master It You’ll have to be able to identify the right people for the job. You’ll need to find a core team you can grow throughout your organization. How do you select the right team for a pilot deployment? Solution Start with a small team of one to two experts. Look for people who have the following attributes: •u Smart and can get things done •u Able to get people excited about a project •u Technically proficient in many areas •u Thorough at collecting and reporting information •u Adaptable and embrace change
Identify a mission and philosophy for your pilot. You’ll need to define goals for your pilot and how you will achieve these goals. Master It You’ll have to define a mission and philosophy for your pilot and communicate it to your team. Solution Create a one-line mission statement for your pilot. Create a one- to two-line philosophy that details how you will achieve that mission. Refer to the “Defining the Mission and Philosophy” section in this chapter. Communicate your mission and philosophy to your team and stakeholders. Select a pilot energy domain. When you begin to execute your pilot, you’ll have to select a small area of management. You should start by dividing your organization into power domains that roughly match metered zones. Master It Take your organization and divide it into manageable units of power consumption. Solution Use a DNS-like naming structure and set up names to partition your organization. You should come up with names that look like com.example.city.building.zone. Identify the basic data sets that you need for your pilot system. You’ll need a system to track the entities in an energy domain. You’ll also need to provide power usage readings for those entities. Master It You’ll need to be familiar with the data fields and formats and know how to collect and report the data.
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Solution Create a spreadsheet that represents the data described in the “Choosing the Data” section of this chapter. Create a separate worksheet for the Entity and Usage classes. Each column should be one of the fields described. Take inventory of a practice area such as your home or a lab. Populate the spreadsheet rows with data from your practice area. Create reports from the data by using the spreadsheet graphing features, and show the reports to your stakeholders to get an idea of what kind of reports you’ll need to create later. Inventory the systems you will deal with and find out how to access the data. The data you’ll gather for your pilot database will come from varying sources. You’ll need to be familiar with the various systems. Master It What are the systems and types of entities in your energy domain? How will you access the data? Solution Create an inventory of the systems in your energy domain. Review the systems listed in the “Gathering the Data” section of this chapter. Review the systems and walk through the features for exporting data.
Chapter 6: Pilot to Production Review your pilot implementation. With your pilot implementation done, you have a chance to review the data and its presentation. Master It You’ll need to review your progress with your stakeholders. How do you ensure that you have the information you need for a rollout? Solution Review the systems and make sure the stakeholders recognize the information. For example, say you did a pilot on a specific lab and then presented the data to the people who use the lab. Do they view the data and respond to it with familiarity? Can they identify the peaks of data and correlate that to the times they were heavily using the pilot devices? If they do, then you know you are reporting the data correctly. Meet with the stakeholders and do the following: •u Examine the reports and data. •u List some scenarios for possible management policies.
Partition your enterprise into manageable rollout areas. You’ll need to select areas to which you will roll out your system within your enterprise. Master It Get to know your campus and start to think about what areas you will convert and when you’ll convert them. What areas and in what order will you roll out to production? Solution Collect diagrams and information from your facilities and network management systems. Identify the physical areas and energy domains. Overlay this with the social and human teams that occupy the space. Create a list of areas and order them according to when you will roll out the system. Review the audit information available from your BMS and NMS systems.
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| Appendix A The Bottom Line Create diagrams that show the rollout areas, and review them with the teams that occupy the spaces. For example, if you have rollout areas for a specific department such as human resources, create a diagram of the area and review it with selected human resources users. Do they recognize the areas, and do they make sense to that team? If the areas you partition seem natural to the people who use them, you know you have a good partitioning. Categorize power-consuming devices in a rollout area. After you’ve selected rollout areas, you’ll have to look at all the devices in that area and categorize them. Master It What are the roles and tags you should use in your rollout? Solution Review the suggested roles for your business types. Make a list of two-word roles that you’ll apply to your devices. Keep it as a master list of roles for all your categorizations. Read it over and review it with other stakeholders. Make sure they understand the categorizations and can answer what type of service the device is providing in your business context. Make a list of tags you can apply to devices. Make sure they can be associated with the social and human context of the area. Keep a master list of tags that you’ll apply to your inventory. Establish a range of importance ratings to rank power-consuming devices. You’ll need to have a set of importance ranges that makes sense for your business. They should be applied to all rollout areas. Master It What are the importance ranges that make sense for your business? Solution Look at the suggested ranges and create a master importance ratings list that you use for all rollout areas. Review them with the teams that occupy the areas. Do the names you selected make sense to the teams? If not, ask them for terms that apply to their teams. For example, a device that is important to a team could be referred to as any one of on-call, ready, hot-line, or standby. Select the terms that are most familiar to the team. Establish a baseline and set up manual policies. For each area you roll out, you’ll need to set up a baseline of power usage. After you set it up, you can devise manual incentives for the occupants of each area. Master It What is the baseline power consumption given your categorization? What kind of manual processes can you put in place? Solution Ensure that your system has reports that can show the utilization reported by energy domains, tags, and roles. Establish a baseline by collecting the monitored data. Talk to the owners and stakeholders in the areas. Set up a friendly competition and rewards for manual processes that improve the baseline. Set up active policies. Active policies can ensure that unattended power controls are enacted. Master It What systems can you modify to control power consumption? Are there areas of your enterprise that can be turned off based on the time of day? Are there any systems that provide APIs? Could you write scripts using these APIs? Solution Set up time-of-day controls in your BMS, NMS, or decentralized systems. Compare your power consumption to the baseline after policies are set up. Roll out active processes after you have gained experience with manual processes.
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Chapter 7: Reporting Recognize government activities for mandated reporting. You’ll need to be familiar with the compliance and regulatory requirements for your business. Master It How do you find out which reporting regulations apply to your business? Solution Review laws and compliance regulations that apply to your business. Start with the U.S. Environmental Protection Agency website. From there you can determine the various other state and local regulations that apply to your business. If you are managing a global operation, you’ll have to consult with your legal and compliance departments in the applicable regions. Make a list of the areas and people with whom you need to maintain contact for this information. Make sure these people are listed as stakeholders in your overall plan. Calculate GHG emissions by converting usage. The data you collect is usage data broken down by business context. The usage data can be converted to GHG emissions. Master It How do you convert usage to GHGs or CDEs? For example, if a department you identified by keyword reported using 2,300 kWh of electricity, what was the CO2 equivalent? Solution Use the calculators provided by the U.S. EPA. By supplying the total kWh usage from your database, you would get 1.7 metric tons of CO2 from the calculator. If you expand the calculations from the EPA, you could equate that to equivalents such as barrels of oil. Select an equivalent that is commonly used by your stakeholder. If your stakeholders are concerned about costs equate the values to dollars. If you have stakeholders concerned with government compliance you may want to select GHG. Choosing an equivalent that your stakeholders are familiar with will have a greater impact to your program. You could provide the same usage information to the calculators offered by Cisco and compare the information to that of other similar companies. Divide your data into time-based sets for detailed reporting. The data you want to track is described as the entity and usage data in Chapter 5. You’ll need to know how to partition it into manageable time-based sets. Master It What types of data should you track over time? If your stakeholders want to budget based on time, such as financial quarters, last hour, or peak usage, how would you report the data? Solution Become familiar with the three main use cases for data. They are live, operational, and historical. Examine reports from your network management and BMS systems. Then ask yourself whether the report you’re looking at is live, operational, or historical. Print out or save important reports and keep them in folders named by these use cases. Examine the reports in your folders, and ensure that you can report that information from your Entity and Usage database. Add fields if needed to the Entity and Usage database that reflect what you need in the three folders of reports. Provide reports directly from your Entity and Usage database that cover what your stakeholders need based on government or financial requirements.
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| Appendix A The Bottom Line Chapter 8: Administering Energy Domains Organize your energy domains in a hierarchy. After you’ve identified the energy domains in your campus, you will need to organize them. Master It How do you create hierarchies of energy domains? Solution Start with your building audits from Chapter 6. Refer to the topologies of your network and buildings, and come up with a naming scheme for your buildings. Keep the naming scheme close to a DNS-like structure. After you’ve identified a naming space, map that to a specific building and group the domains based on their use. Take a look at some of the names you created out of context and away from any drawings. Can you identify where a domain is in your business? For example, a name such as com. example.p8.panel7.j5 does not make much sense out of context. A better name, such as com. example.building8.panel8.lab5, starts to denote a hierarchy in your enterprise. Classify your devices according to consumption type. You’ll need to create a set of classifications for your consumers of power. Master It What are the different devices in your enterprise? Solution Extract the data you recorded in the previous pilots and create a report by domain of the devices by type. Try to see whether there are common sets of devices used by people at that location. Are there teams of people that use certain devices exclusively? If so, group them by adding keywords. Master It What are the classification types for your enterprise? Solution Start with the basic classifications in Table 8.2. Examine the listing of devices in your pilot, and see whether they map to the basic classifications. If you need more types or subtypes, add them to the hierarchy. Do they conform to the Is a type of test? If so, create a reference list of types. Classify and create a master list of policies. You’ll need to create a master list of policies and determine how to fulfill them in your enterprise. Master It What types of rules can you apply to your classified types? Solution Take a look at the device types and classifications. Look at the physical domains of which they are a part. Then try to determine the business context based on their role, importance, and keywords. If the keywords define a particular department or division of your enterprise, conduct interviews with the stakeholders from those departments. Try to determine which devices are not needed and the schedules of the users of the devices. Create a master list of policies similar to Table 8.5 that can be mapped to the usage patterns of the stakeholders and people you interviewed. Map the master policies in a table similar to Table 8.7 and review that table with the users of the devices. Do they see any conflicts? Would they be interrupted or inconvenienced by the policy? If so, create new policies or mark them as not enforced.
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Chapter 9: Making Your Program Sustainable Determine what funding model is the best fit for your organization. You will need to fund an energy management program in a way that will ensure your program’s sustainability. Choosing a funding model with limited disruption to the organization and limited complexity is preferable. Master It Meet with the business leaders across the IT, facilities, and finance departments. Ask which options addressed in this chapter are most interesting to them. Establish a basic financial model for your program, include projected savings, and present it back to these leaders to gain confirmation of their support. Solution Build out a master spreadsheet that shows the total costs and total savings your program will achieve. Break out the totals into logical groupings of assets that are aligned to the existing management teams (facilities and IT). Project savings as your program scales across assets. Build summarization charts and structure them into an executive presentation (PowerPoint) and keep this roll-up consistent across your relevant stakeholders. Implement a programmatic model that can scale over time. The structure of your program will be as important to its sustainability as a funding source. Your team’s form will follow its function, so take care to map the right skill sets to the deliverables of the team. The biggest cost of your program will be staffing or professional services if you can’t find the right skill sets internally. Choosing the right structure will be specific to your operations but, where possible, keep it simple yet extensible. Master It As part of your interviews with business stakeholders to uncover funding sources, also look into existing organizational structures and operative models. Build a simple list of the pros and cons for the structures detailed in this chapter. Take this same list and project it three to five years out and see what investments are required to scale it. Solution Build a detailed diagram of what a particular programmatic model provides, what it requires to operate, how it operates, who operates it, and where it is administered. If it is not clear to you how your program should be structured, having all this information in one place will help.
Chapter 10: Preparing for the Next Big Thing Identify and understand adjacent technologies, resources, and operations that might help your program add value. Continue to develop your understanding of core program deliverables while identifying new application areas, sources of funding, and talent to ensure that your program can scale. Master It Put aside dedicated time with your team’s more-strategic thinkers. Conduct sessions with a scribe to go through multiple levels of mapping across technology, people, processes, and finance. Aggregate the content from these planning and mapping sessions, and hold annual review sessions that set the strategic direction of your program. Solution Start to build out content that is relevant to the strategic direction of your program, hosted in a collaboration space. Content vehicles can include mind maps,
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| Appendix A The Bottom Line organizational charts, process flows, program milestone calendars, PowerPoint presentations, and SketchUp reference designs. Assess the user behavior and reporting implications of your program both internally and externally. There are bound to be some surprises as your program scales. Where possible, try to anticipate the less-tangible impacts of your program. Employing scenario-modeling techniques can help mitigate the unintended consequences of a well-meaning program. Master It Look for team members (both virtual and core) who challenge your thinking. Foster open discussion and objective feedback with these members through semi-regular steering meetings. Conduct these sessions in advance of major decisions that might affect the strategic direction of your program. Solution Develop a simple, modular PowerPoint presentation that captures assumptions, proposed strategies, and technologies along with a simple pros and cons list for each. Build this content over time into a four-quadrant chart that takes into account high and low values contrasted against high and low costs. Build this presentation out over a future timeline and capture the likely impacts of plausible scenarios for your program. Become a leading energy manager in the industry today. As the market for Green solutions matures, many opportunities for growth and change will arise. You will want to stay at the forefront of this quickly evolving industry. Continually improving your skills across energy and related resources will ensure that you stay professionally differentiated. Master It Look for opportunities to participate in shared learning exchanges. Seek out peers in core and adjacent fields of management and solutions development. Commit to filling gaps you identify in your own knowledge base and skill set. Define what a career in energy management means to you, and set personal goals to become a leader in energy management services. Solution Each person has their own system, but we found maintaining a spirit of “geeking out” with fellow technologists and going Green in every way practical in our personal lives helps tremendously. Use the Web 2.0 resources that are available to branch out your research, networks of professionals, and solutions portfolio. Challenge the status quo of what innovation toward improved resource management ultimately means and help to drive the industry forward. We have developed a site called IP Energy Management Services that will help us learn from and share with our readers. The site can be found at www.ipenergyservices.com. Please join us in the discussion here.
Appendix B
Links and Resources The following are some resources that we used in writing this book. We’ve tried to address terms and concepts that will be relevant to developing your knowledge base for energy management. In addition, we’ve included useful tools that will serve as ready references and self-study resources.
Useful Software Both authors are Mac users and wrote this book using Microsoft Word for Mac. Graphics were created with a combination of Google SketchUp, Microsoft Visio, Microsoft PowerPoint, and Apple OS X applications. We tried where possible to use existing tools that are free and open source. To that end, we’ve compiled a list by chapter topic of the tools we’ve used in building the key elements of an energy management program. Chapter 1: A Stake in the Ground The usual professional office packages available from Microsoft and more recently from Google will help to create your analytics: http://office.microsoft.com http://docs.google.com
Chapter 2: Benchmarking The usual professional office packages should suffice. For morecomplex modeling on larger data sets, the free database MySQL is very helpful: www.mysql.com
Chapter 3: Assessing Value Tools from the first two chapters will apply here, but some additional planning and collaboration tools might be useful. For organizing your plans and notes, MindMeister is a useful application: www.mindmeister.com
For collaboration, document sharing, and conferencing, Cisco WebEx is a valuable resource: www.webex.com
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| Appendix B Links and Resources Chapter 4: Managing Your Project Where possible, use the existing project management tools for your organization, as we recommended in this chapter. If you want to explore other systems, we found Basecamp and 5pm to be helpful: http://basecamphq.com www.5pmweb.com
Chapter 5: Building a Pilot Deployment The focus of this chapter is on creating a pilot team and pilot root system. The best tools to use are the ones with which your team is comfortable. In our pilot deployment, we found that using virtual machines was a key to rapid development for the base platform of the pilot system. For operating systems and bundled applications, we used LAMP (Linux, Apache, MySQL, and PHP) virtual appliances so that we had basic services and reporting already available. Although there are many free tools, we used the ones offered from VMware: www.vmware.com/appliances
Chapter 6: Pilot to Production The focus of this chapter is defining and populating your pilot with energy-management-related data from your enterprise. We found that the most useful way to perform this exercise was to create simple Microsoft Excel spreadsheets to prepare information for use in MySQL: http://office.microsoft.com www.mysql.com
Chapter 7: Reporting The focus of this chapter is creating reports from your pilot data. Most databases come with reporting tools. Having reports that are interactive and attractive will help in presenting your pilot to stakeholders. We relied heavily on Adobe Flex: www.adobe.com/products/flex/
We also became big fans of the work done by the BirdEye group. They created a lot of useful open source reporting widgets for Adobe Flex that helped us in representing information in an intuitive and professional way: http://birdeye.googlecode.com/
Chapter 8: Administering Energy Domains The focus of this chapter is on partitioning and organizing your enterprise so that you can classify and manage your energy-consuming devices. As in Chapter 6, the simplest way to get a handle on this information is to use spreadsheets to model it: http://office.microsoft.com
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We also reference basic network topologies and best practices that are publicly available through Cisco’s Design Zone program: www.cisco.com/go/designzone
Chapter 9: Making It Sustainable The focus of this chapter is more about integration with existing systems than it is about standing up new tools. However, we found the databasecentric tools from OSIsoft to be very helpful with integration and normalization of data from utility systems. OSI provided us with an evaluation unit. It was integral to our benchmarking and throughout our program development: www.osisoft.com
Chapter 10: Preparing for the Next Big Thing The focus here was more about research than about modeling or reporting. The best tool we used for the focus of this chapter was Google Search: www.google.com
Infrastructure and Efficiency Modeling Although not directly featured in the book, behind the scenes we used Google SketchUp extensively. This package is amazingly powerful for a free tool and is natively conversant with Google Earth: http://sketchup.google.com
We often built SketchUp models to show a before-and-after scenario of lab and data center environments. To get up to speed on SketchUp, we used the online tutorials and recommended this to our teams: http://sketchup.google.com/training
Mac-to-Windows Compatibility Because we wrote this book using Macs, we did need to convert between OS X and Windows applications, particularly adapting Visio drawings to Apple graphics packages. We found that VMware Fusion provided the best all-around functionality. Although the program is not free, we found it was worth the investment: http://downloads.vmware.com/d/info/desktop_downloads/vmware_fusion_for_the_mac/3_0
In general, we needed to have flexibility in what data and formatting we could use. There were many types of data we needed to gather, normalize, and model. This was one of our primary reasons for using the Mac. Apple platforms give us the ability to customize our tool sets in support of many “odd jobs.” However, we did need to spend some extra time ensuring that our stakeholders could consume the content we prepared in Microsoft formats.
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| Appendix B Links and Resources Topical Resources The following are some of the resource links that are related to the research and technologies discussed in the book. These resources are intended to be high level and suitable to introduce the topics we discussed in the book.
Research, Analysis, and Educational Resources There are so many resources available on the economics of global energy use that the challenge is often to weed through them all to get down to what is important to you. Here are some good sites to which we often referred in the course of writing this book: •u The main website for the U.S. Department of Energy’s Energy Information Administration
(EIA) is in our opinion the most extensive resource on the topic globally: www.eia.doe.gov •u In Europe, we found the European Union websites to be best for high-level analysis. For
more-specific information, we opted for individual country energy sites. Europe’s Energy Portal is a good place to start: www.energy.eu •u In the Asia-Pacific region, we used the Japan Institute of Energy Economics much as we
used the U.S. DOE EIA as a proxy for regional data: www.ieej.or.jp •u Although we had the funding to use only public domain information in building our pro-
gram, we found some IT analyst firms that are doing interesting work around Green IT. The 451 Group and McKinsey & Company are very active, and giants such as Gartner and IDC are dabbling in key areas as well: www.the451group.com www.mckinsey.com www.gartner.com www.idc.com •u A bevy of emerging research and analysis exists on energy, carbon, and resource security
within universities and governmental organizations. However, we found none that analyzed the wholesale implications of IP-enabled energy management. Nonetheless, pieces of the puzzle can be found across many agencies, such as these: United Nations (UN) World Resources Institute (WRI) International Energy Agency (IEA)
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Environmental Protection Agency (U.S. EPA) Lawrence Livermore National Laboratory (LLNL) U.S. National Renewable Energy Laboratory (NREL) U.S. Green Building Council (USGBC) BRE Environmental Assessment Method (BREEAM) Lawrence Berkeley National Laboratory (LBL) We also used many Web-2.0-type tools to educate ourselves about current thinking on energy, economics, security, and alternative design. There are, of course, many social networks that have sprung up, and some are focused on professional associations while others focus on education. We found some of these particularly useful: iTunes University (iTunes U) There is no easier way to get the current thinking on all things Green than iTunes U. Many world-class institutions are sharing valuable course work and educated discourse on a variety of topics here. We recommend the Massachusetts Institute of Technology’s course work for all our team members because it gives IT professionals a quick and easy way to learn the basics of electrical engineering. MIT, Harvard University, the University of California at Berkeley, Stanford University, Yale University, and Purdue University are providing interesting and educational discourse on all kinds of issues related to energy management. LinkedIn Although it has its fair share of marketing, the LinkedIn site is a great way to stay current and connected with others who share technological, ecological, and economic interests. We are members of several groups that foster shared learning: CleanTech.Org Linked: Energy GREENNET Data Center Pulse GreenBiz.com UptimeInstitute
Online Tools There are many tools we use in our day jobs that influenced this book. Although some of these might not apply to what you are seeking to accomplish, they are worth mentioning. We caution that most online tools use assumptions in their calculations. Be sure you understand any of these assumptions and check them for accuracy.
Power and Cooling Calculators The industry has little standardization, but some handy tools we use are as follows: •u APC Power Calculator:
http://tools.apc.com
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| Appendix B Links and Resources •u Cisco Green Data Center Model Calculator:
www.cisco.com/go/efficiency •u Cisco Product Efficiency Calculator:
www.cisco.com/go/efficiency
Building Energy Analysis Many of these tools focus on the residential level, but a handful are useful for large enterprises: •u OpenEco:
www.openeco.org •u U.S. Green Building Council’s LEED Checklists:
www.usgbc.org/DisplayPage.aspx?CategoryID=19 •u BREEAM Extranet for the Green Guide:
www.bre.co.uk/greenguide
Greenhouse Gasses We limited our GHG scope to focus on only equivalent carbon dioxide (CO2e). To that end, we needed only those sites that could help us convert watts to CO2e. We found that the U.S. EPA’s eGRID website provided the conversion factors we needed: •u EPA eGRID:
www.epa.gov/cleanenergy/energy-resources/egrid/index.html •u The Greenhouse Gas Protocol Initiative:
www.ghgprotocol.org/calculation-tools/all-tools •u EPA Power Profiler:
www.epa.gov/cleanenergy/energy-and-you/how-clean.html
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Data Center Energy Use •u The Green Grid:
www.thegreengrid.org •u Lawrence Berkeley National Laboratory:
http://hightech.lbl.gov/datacenters.html
Energy Efficiency •u Climate Savers Computing:
www.climatesaverscomputing.org/tools •u U.S. Department of Energy—Energy Efficiency & Renewable Energy:
www.eere.energy.gov •u 1E Online Energy Saving Calculator:
www.1e.com/energycampaign/Calculation.aspx
Sustainable Technologies •u U.S. National Renewable Energy Laboratory:
www.nrel.gov •u U.S. Environmental Protection Agency Sustainable Technologies:
www.epa.gov/nrmrl/std •u National Geographic’s Green Guide:
www.thegreenguide.com •u TreeHugger, a Discovery company:
www.treehugger.com
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| Appendix B Links and Resources The Authors’ Websites •u This book’s site:
www.ipenergyservices.com
Rob Aldrich •u Professional work/blog:
http://blogs.cisco.com/green http://blogs.cisco.com/datacenter http://www.cisco.com/go/efficiency •u Personal site:
www.robaldrich.com •u YouTube:
www.youtube.com/greencisco •u SketchUp username:
Mrgreen
John Parello •u Professional:
www.cisco.com/go/energywise •u Personal site:
www.johnparello.com
Glossary Common term definitions are courtesy of Merriam-Webster’s Open Dictionary and Wikipedia.
A Active Energy Management A management construct that balances the supply of electricity with the demands of IT systems. The term active here denotes a system that has the capability to measure, monitor, and manage electrical and thermal requirements. In the context of this book, active energy management is a broad term. It does not refer to IBM Systems Director Active Energy Manager.
Active Thermal Management A management construct that balances the supply of chilled air or water with the heat-removal demand of IT systems. The term active here denotes a management system that is not solely reliant on legacy thermostatic controls.
Admission Control (or Network Access Control) A set of protocols and/or policies that describes how to secure access to a network or network nodes by inhibiting attempts to access the network.
Advanced Technology Extended (ATX) A computer form-factor specification developed by Intel in 1995. The specification defines the key mechanical dimensions, mounting point, I/O panel, power, and connector interfaces between a computer case, a motherboard, and a power supply.
Ampere (Amp) The practical meter-kilogram-second unit of electric current that is equivalent to a flow of 1 coulomb per second or to the steady current produced by 1 volt applied across a resistance of 1 ohm.
Analysis Paralysis A state that occurs as a result of spending too much time on fine-level details and not enough time on
the larger context of a goal. In this book, the term is used to describe a situation where data is being debated in advance of common measurement capabilities for electrical consumption related to IT.
Asset/Inventory List A list of electrical devices in an enterprise that draw power. These lists are the tracking and, in many cases, the foundation for billing systems for corporate assets. These lists can be as simple as a spreadsheet or as complex as an enterprise database. In the context of this book, they are referred to as a source of data in establishing benchmarks and a business case for energy management.
Asset Utilization For both facilities and IT assets, this refers to percentage of total workload at which an asset is operating.
Availability 1. The degree to which a system, subsystem, or equipment is operable and in a committable state at the start of a mission, when the mission is called for at an unknown, that is, a random, time. Simply put, availability is the proportion of time a system is in a functioning condition. Note 1: The conditions determining operability and committability must be specified. Note 2: Expressed mathematically, availability is 1 minus the unavailability. 2. The ratio of (a) the total time a functional unit is capable of being used during a given interval to (b) the length of the interval. Note 1: An example of availability is 100/168 if the unit is capable of being used for 100 hours in a week. Note 2: Typical availability objectives are specified either in decimal fractions, such as 0.9998, or sometimes in a logarithmic unit called nines, which
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| Glossary corresponds roughly to a number of nines following the decimal point, such as five nines for 0.99999 reliability.
geographic information, and quantities and properties of building components.
B
Sometimes referred to as a Building Automation System (BAS), a Building Management System is a computer-based control system installed in buildings that controls and monitors the building’s mechanical and electrical equipment, such as ventilation, lighting, power systems, fire systems, and security systems. A BMS consists of software and hardware; the software program, usually configured in a hierarchical manner, can be proprietary using such protocols as C-Bus, Profibus, and so on. Vendors are also producing BMSs that can be integrated into IT management dashboards using Internet protocols and open standards such as DeviceNet, SOAP, XML, BACnet, and Modbus.
Bill-Back Models Corporate accounting systems that allocate costs to business units based on the group’s subscription level for a shared IT service.
Branch Circuit Monitoring Sometimes referred to in the context of a branch circuit monitoring system (BCMS), these hard instrumentation instruments are installed within a circuit breaker panel and measure electrical usage (typically amperage) of individual circuits.
BRE Environmental Assessment Method (BREEAM) A voluntary measurement rating for Green buildings that was established in the United Kingdom by the Building Research Establishment (BRE).
British Thermal Unit (Btu) A unit of energy indicating the quantity of heat required to raise the temperature of 1 pound of water 1 degree Fahrenheit at a specified temperature (such as 39°F).
Broadcast Domain A logical division of a computer network in which all nodes can reach each other by broadcast at the Data-Link layer. Any computer connected to the same Ethernet repeater or switch is considered a member of the same broadcast domain. (See DataLink Layer.)
Building Information Modeling (BIM) The process of generating and managing building data during its life cycle. Typically, it uses threedimensional, real-time, dynamic-building modeling software to increase productivity in building design and construction. The process produces the Building Information Model (BIM), which encompasses building geometry, spatial relationships,
Building Management System (BMS)
C Capacity Management A process used to manage information technology (IT). Its primary goal is to ensure that IT capacity meets current and future business requirements in a cost-effective manner.
Carbon Dioxide Equivalent (CO2e) A distinct measure for describing how much global warming a given type and amount of greenhouse gas may cause using the functionally equivalent amount or concentration of carbon dioxide (CO2) as the reference. In this book, the term is used as a translation of CO2 resulting from wattage consumed.
Comma-Separated Values (CSV) A simple data file or data format in which each record is on one line, and each field is separated by a comma.
Computational Fluid Dynamics (CFD) One of the branches of fluid mechanics that uses numerical methods and algorithms to solve and analyze problems involving fluid flows. Computers are used to perform the millions of calculations required to simulate the interaction of liquids and
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gasses with surfaces defined by boundary conditions. CFD is based on Navier–Stokes equations, which define any single-phase fluid flow.
Computer Room Air Conditioning (CRAC) Specialized HVAC systems that support the white space of a computer room or data center. A CRAC system is not designed or deployed for general comfort or building cooling. A CRAC system’s primary function is to remove heat from IT systems.
Corporate Average Data Center Efficiency (CADE) Introduced in July 2008 by McKinsey & Company in conjunction with the Uptime Institute, the CADE metric builds on the power usage effectiveness (PUE) framework but takes into account IT asset utilization. The formula for CADE is as follows: CADE = Facility Efficiency (FE) × Asset Efficiency (AE) Facility Efficiency (FE) = (Facility Energy Efficiency) × (Facility Utilization) Asset Efficiency (AE) = (IT Energy Efficiency) × (IT Utilization)
Corporate Social Responsibility (CSR) Also known as corporate responsibility, corporate citizenship, responsible business behavior, sustainable responsible business (SRB), and corporate social performance, it is a form of corporate self-regulation integrated into a business model. Ideally, CSR policy functions as a built-in, selfregulating mechanism whereby businesses monitor and ensure that they support the law, ethical standards, and international norms.
D Database An organized collection of data that is typically managed by a software system providing storage, access, security, and other facilities.
Data Center Infrastructure Efficiency (DCiE) A performance-improvement metric used to calculate the energy efficiency of a data center. DCiE is the percentage value derived by dividing information technology equipment power by total facility power.
Data-Link Layer Layer 2 of the seven-layer OSI model of computer networking. It corresponds to, or is part of, the Link layer of the TCP/IP reference model.
Designed Availability The availability target or goal within the planning process of an IT asset deployment. The targeted design availability differs from the resilience of an asset or architecture in that availability is defined in terms of expected outages, and resilience refers to how long it takes get an asset back online. Availability of assets is often measured in mean time between failures (MTBF).
Designed Efficiency The efficiency target or goal within the planning process of an IT asset deployment. The targeted design efficiency differs from the operative efficiency of an asset or architecture based on live instrumentation.
Distribution Voltage The global voltage ranges (100 V to 480 V) for distributing electrical supply from generation to end user, including IT systems.
E Emergency Power Off (EPO) Typically found in lab and data center environments, an emergency power off switch provides the capability to turn off electrical supply to a zone or the entire floor of a data center. The switch is operated by pushing a large button, usually colored red and often located near the entrance/exit of a data center floor.
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Energy Management
A term used to describe a device that connects to or uses network connectivity. In contrast to a network node, which is a device that combines with other devices to create a network, an endpoint is a leaf node or the termination device attached to a network.
A construct that spans the macro/micro-economic scale. It generally refers to the people, processes, and technology that support the management of energy creation (electrical generation), transmission, and storage. In the context of this book, the term is used to describe any number of concerted efforts, from individuals all the way up to nations, to monitor, measure, and manage electrical, thermal, and hydrologic energy.
Energy Allocations Typically refers to cost allocation for departmental electrical billing. In this book, this term is used as a method for estimating and summarizing electrical savings by asset, department, operation, and/or architecture.
Energy Policies A set of rules or actions that describe how and/or when a device should consume or produce energy.
Energy as a Service (EAAS)
Energy Security
Energy management being delivered as an IT service.
A very broad term with many interpretations but used in this book to describe a company or nation considering the sustainability of the sources of electricity needed to support mission-critical functions.
Energy Domain A set of devices that consumes or produces electricity and is managed as one unit. For example, all devices in a specific building can be considered a single energy domain.
Energy Efficiency Energy conversion efficiency is the ratio between the useful output of an energy conversion machine and the input, in energy terms. It is a loosely defined concept with many interpretations; energy efficiency typically refers to using less energy to provide a comparable level of energy service. In the context of this book, energy efficiency is a super set of energy management and can be applied through people, processes, and technologies.
Energy Intensity A macroeconomic measure of the energy required per unit of economic output. It is commonly expressed as units of energy per unit of gross domestic product (GDP). Source: http://www.gcrio.org/ipcc/techrepI /appendixe.html
Energy Sourcing The practice of considering, in detail, what types of fuel sources are available for electrical supply as part of a site selection effort. In the context of this book, energy sourcing is addressed as a way to reduce an operation’s GHG contributions for future office and data center locations.
Energy Usage Effectiveness (EUE) A metric introduced by the U.S. EPA, EUE is calculated by dividing total source energy by total UPS (usage per square foot) energy. The EPA is using source energy instead of site energy to be able to account for a variety of fuel types a facility uses in its energy-efficiency equation. Those fuel types may include things such as chilled water delivered from the utility. Source energy also accounts for losses that result from conversion and transport of energy to the site.
Entity Class A set of data defined in this book as describing a consumer or producer of energy.
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F Facilities A broad term that, for the purposes of this book, focuses on the facilities industry, departments, facilities assets, and professionals. The facilities industry as termed by IT professionals typically refers to technologies and practices that directly support IT systems. The facilities industry is a subset of the building and real estate industries. A more-accurate denotation of facilities that is often used is mission-critical facilities.
Facilities Overhead Generically refers to the amount of electrical capacity and cost that is allocated to missioncritical and/or building assets. In the context of this book, the term is used to align and profile the facilities energy requirements of groupings of IT assets across an enterprise.
FCAPS The ISO Telecommunications Management Network model and framework for network management. FCAPS is an acronym for Fault, Configuration, Accounting, Performance, and Security.
gasses, called greenhouse gasses. They transfer this energy to other components of the atmosphere, and it is re-radiated in all directions. This transfers energy to the surface and lower atmosphere so the temperature there is higher than it would be if direct heating by solar radiation were the only warming mechanism. This mechanism is fundamentally different from that of an actual greenhouse, which works by isolating warm air inside the structure so that heat is not lost by convection.
Greenhouse Gasses (GHGs) Gasses in an atmosphere that absorb and emit radiation within the thermal infrared range. This process is the fundamental cause of a greenhouse effect. The main greenhouse gasses in the Earth’s atmosphere are water vapor, carbon dioxide, methane, nitrous oxide, and ozone.
Graphical User Interface (GUI) The means by which a person interacts with software running on a device. This is the same principle behind the human machine interface (HMI) terminology used in the facilities industry.
H
Flex Days
Hard Instrumentation
A term used in California to describe days on which electrical utility capacities are strained to the point that rolling blackouts are imminent.
A term used in this book to describe an instrumentation methodology that relies on an external hardware component to measure the electrical usage of IT and facilities assets. Examples of hard instrumentation are smart PDUs and branch circuit monitoring.
G Green In this book, Green denotes a concerted effort to reduce the environmental impact of an activity, practice, product, or component. This book focuses on the subset of mitigating the impact of GHGs that result from electrical usage through improved energy management.
Greenhouse Gas Effect A process by which radiative energy leaving a planetary surface is absorbed by some atmospheric
Heating, Ventilation, and Air Conditioning (HVAC) Environmental comfort systems applied in the context of real estate. This term broadly encompasses all the internal environmental requirements for comfort heating and cooling. HVAC is particularly important in the design of medium to large industrial and office buildings such as skyscrapers and in marine environments such as aquariums, where safe and healthy building conditions are regulated
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with temperature and humidity, as well as “fresh air” from outdoors.
Heliostatic Solar Electrical generation plants that use mirrors to concentrate solar energy in order to create steam and drive a turbine. These plants do not use photovoltaic technology.
Human Machine Interface (HMI) The term used in the facilities industry to describe the means by which a person interacts with a control system console. This is the same principle behind the graphical user interface terminology used in the IT industry.
I Information Lifecycle Management (ILM) A wide-ranging set of strategies for administering storage systems on computing devices. ILM comprises the policies, processes, practices, and tools used to align the business value of information with the most appropriate and cost-effective IT infrastructure from the time information is conceived through its final disposition. Information is aligned with business processes through management policies and service levels associated with applications, metadata, information, and data.
Information Technology (IT) The department, operations, assets, and professionals involved in the information technology industry. This term is interchangeable in common parlance with information and communications technology (ICT).
Internet Protocol (IP) A protocol used for communicating data across a packet-switched internetwork using the Internet Protocol Suite, also referred to as TCP/IP. IP is the primary protocol in the Internet layer of the Internet Protocol Suite, and it has the task of delivering distinguished protocol datagrams (packets) from the source host to the destination host solely based on their addresses.
IP Backbone A term used in both the facilities and IT industries, an Internet protocol backbone refers to a network or collection of networks that rely on IP as the primary means of communications.
IT Services In contrast to what are often called professional services, IT services are offered to business units in a corporate context for tools that support a business function such as email, payroll, and data storage.
J Joules The joule, named after James Prescott Joule, is the derived unit of energy in the International System of Units. It is the energy exerted by the force of 1 Newton acting to move an object through a distance of 1 meter.
K Kilovolt-ampere (kVA) A unit of measurement used for the empirical power in an electrical circuit equal to the product of root-mean-square (RMS) voltage and RMS current. In direct current (DC) circuits, this product is equal to the real power (active power) in watts.
Kilowatt-hour (kWh) A unit of energy equal to 1,000 watt hours or 3.6 megajoules (symbol kW·h, or kWh).
L Leadership in Energy and Environmental Design (LEED) Developed by the U.S. Green Building Council (USGBC), LEED is an internationally recognized Green building certification system, providing third-party verification that a building or community was designed and built using strategies intended to improve performance in metrics such
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as energy savings, water efficiency, CO2 emissions reduction, improved indoor environmental quality, and stewardship of resources and sensitivity to their impact.
M Managed Services A fairly broad term used in the IT industry to describe a professional service model that involves a degree of systems convergence and/or systems hosting delivered to a user by a vendor or systems integrator. Managed services is the practice of transferring day-to-day related management responsibility as a strategic method for improved effective and efficient operations.
years, the pace slowed down a bit, but data density has doubled approximately every 18 months. This is the current definition of Moore’s law, and Moore himself has blessed it. Most experts, including Moore, expect Moore’s law to hold for at least another two decades.
N Nameplate Power Draw Generally refers to the power requirement of an IT or facilities asset based on a data sheet rating. This data sheet rating is typically based on the total maximum power capability of an asset and does not represent the nominal power requirement of an asset, which can be as much as 30 percent less.
Massively Scalable Data Centers (MSDCs)
Network Core
A new term to the IT industry used to describe very large data centers typically operated by very large Internet service providers (ISPs) and search engine providers. These data centers are typically built in a highly modular, nonstop model in which failover is accounted for against large blocks of computing, networking, and storage capacities.
The central switches and routers in a network that provide connectivity between local area networks or access switches.
Microbial Fuel Cell (MFC) A bio-electrochemical system that drives a current by mimicking bacterial interactions found in nature. Also called a biological fuel cell.
Micro-Grids Also used in this book in the context of a smart load, this term describes networked, powerconsuming devices that can communicate with a command and control system in order to govern energy usage.
Moore’s Law The observation made in 1965 by Gordon Moore, co-founder of Intel, that the number of transistors per square inch on integrated circuits had doubled every year since the integrated circuit was invented. Moore predicted that this trend would continue for the foreseeable future. In subsequent
Network Edge Generally refers to the network infrastructure and logical application of networking closest to end-use devices. The infrastructure found at the network’s edge typically includes PoE switches, branch switching, and routing.
Network Management System (NMS) A software system that monitors and controls network elements.
Nominal Power Draw The power requirement of an IT or facilities asset that is based on tested and measured power draws for the power supplies within the asset.
O Object-Oriented A paradigm that uses data structures as objects with associated methods or actions to design applications or computer programs.
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| Glossary Operative Efficiency The efficiency of an IT asset or architecture. Expressed as a percentage of total electrical usage, this term is used to delineate efficiency in operations versus the designed efficiency of an architecture or asset.
Overcooling A common scenario for legacy data center environments in which too much cooling is being provided. This is often the result of inaccurate planning and is due to availability considerations.
P Performance Contracting A professional service framework that is funded based on a percentage of cost savings resulting from energy-efficiency measures. Facilities and real estate vendors typically offer performancecontracting options that focus on buildingefficiency technologies such as high-efficiency lighting and variable frequency drives.
Photovoltaic (PV) Also called solar cells and solar panels, PVs are arrays of cells containing a solar photovoltaic material that converts solar radiation into direct current electricity.
Pilot System When designing a new kind of system, a team will design a throw-away system (whether it intends to or not). This system acts as a pilot plant, and it reveals techniques that will subsequently cause a complete redesign of the system.
Portal A website or system that functions as a point of access to information in other systems or websites. An intranet portal is the gateway that unifies access to all enterprise information and applications on an intranet. It is a tool that helps a company manage its data, applications, and information more easily and through personalized views. Some portal solutions today are able to integrate legacy applications,
other portals’ objects, and handle thousands of user requests. For enterprise users, a portal is also known as an enterprise portal.
Power over Ethernet (PoE) A system that safely passes electrical power, along with data, on Ethernet cabling. The power is typically provided by an Ethernet switch.
Power State Levels As defined in this book and Cisco EnergyWise, power state levels describe a set of scaled conditions or states to which a device can be set; the power consumption will vary between states.
Power Usage Effectiveness (PUE) A measure of how efficiently a computer data center uses its power—specifically, how much of the power is actually used by the computing equipment (in contrast to cooling and other overhead).
Process Line Controls (PLCs) A broad term used in the facilities industry to describe microprocessor-controlled devices within a SCADA system, which can monitor and control process points in another system. These devices are typically applied in support of mechanical and industrial automation systems.
Q Quality of Service (QoS) In the field of computer networking and other packet-switched telecommunication networks, this traffic engineering term refers to resource reservation control mechanisms rather than the achieved service quality. Quality of service is the ability to provide different priorities to different applications, users, or data flows, or to guarantee a certain level of performance to a data flow.
R Remote Terminal (or Telemetry) Unit (RTU) An RTU is a microprocessor-controlled electronic device that interfaces objects in the physical world
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to a distributed control system or SCADA system by transmitting telemetry data to the system and/ or altering the state of connected objects based on control messages received from the system.
root of the arithmetic mean (average) of the squares of the original values (or the square of the function that defines the continuous waveform).
Renewable Energy Credits (RECs)
A system that provides a user portal or summary for other systems.
Also known as renewable energy certificates, Green tags, renewable electricity certificates, or tradable renewable certificates (TRCs), these are tradable, nontangible energy commodities in the United States that represent proof that 1 megawatthour (MWh) of electricity was generated from an eligible renewable energy resource (renewable electricity).
Resilience The property of a material to absorb energy when it is deformed elastically and then, upon unloading, to have this energy recovered. In other words, it is the maximum energy per unit volume that can be elastically stored. It is represented by the area under the curve in the elastic region in the stressstrain diagram. Modulus of Resilience, Ur, can be calculated using the following formula: Ur= σσ2/(2E) = 0.5 σ εε where σσ is yield stress, E is Young’s modulus, and εε is strain Within the IT industry, this term is often applied to data center environments, where it is used to define the amount of time it takes to recover from an unexpected systems failure. The mean time to recovery (MTTR) for a component or product is often associated with resilience.
Role As defined in this book and Cisco EnergyWise, a role indicates the function that a device provides in order to specify the business context of the device.
Root-Mean-Square (RMS) Voltage A statistical measure of the magnitude of a varying quantity. It is especially useful when variations are positive and negative. The RMS value of a set of values or a continuous-time waveform is the square
Root System
S Sensor Networks A network that is specifically designed to capture and transport data derived from specialized sensors such as temperature, humidity, barometric pressure, flow rate, seismicity, and toxicity. An SN consists of spatially distributed autonomous sensors to monitor physical or environmental conditions cooperatively, such as temperature, sound, vibration, pressure, motion, or pollutants.
Shutdown In the context of IP-enabled energy management, shutdown refers to the complete electrical shutdown of an IT or facilities asset.
Simple Network Management Protocol (SNMP) A communication standard used to monitor and manage network devices. The protocol defines communication methods as well as data objects.
Smart Grid This is the contemporary concept of taking the current loose affiliation of regional electrical grids and improving their management capabilities through information and communications technologies.
Smart Loads Also called micro-grids, these are groupings of IT infrastructure that can natively report their energy use to an aggregate point and also accept commands to shutdown or turn off their electrical requirements. Smart loads typically rely on software only rather than external hardware control points such as a smart PDU.
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| Glossary Smart PDUs A power distribution unit (sometimes called a power rail) that is typically installed within an IT equipment enclosure (rack) and that has a network interface card (NIC) within the PDU. There are many versions of these PDUs, but all will typically provide for power monitoring and control at the rack level.
Soft Instrumentation In the context of this book, soft instrumentation refers to software that communicates natively through Internet protocols for the purpose of energy measurement, monitoring, and management.
Source, Fate, and Transfer Generally refers to the process of identifying the source of a pollutant, its deposition location, and the path it takes to move from its source. Most often applied in the environmental sciences, it is mentioned in this book as a process that could be enabled by Internet protocols in the near future. Secondarily, it refers to an analogy of the same process but in the context of electrical consumption.
Spin-Down In the context of energy management, spinning down a facilities or IT asset refers to the ability of a software-based interface to disable certain asset capabilities in order to use less power but not completely shut down the asset.
Structured Query Language (SQL) A computer language designed for managing data in relational database management systems. Based on relational algebra, it includes data insert, query, update, delete, schema creation, and modification.
Submeter A meter typically installed by a property owner that monitors a subset of the electricity metered by the utility for that property owner.
Supervisory Control and Data Acquisition (SCADA) A network architecture that is prevalent in industrial automation, process line controls, building
management systems, water monitoring systems, and electrical grid management. There are many types of communications protocols used in SCADA such as Modbus, Profibus, DNP3, and OLE for Process Control (OPC).
Switched-Mode Power Supply (SMPS) An electronic power supply typically used in IT assets that incorporates a regulator in order to improve efficiency. Like other types of power supplies, an SMPS transfers power from an electrical source to an asset while converting voltage and current characteristics.
T Telepresence A high-definition, low-latency video collaboration solution from Cisco. In the context of this book, the term refers to an offering that can help a company implement travel reductions.
Tin Cup A slang term referring to the corporate or governmental process of aggregating funding across related organizations. This funding is often applied to a program that will deliver some degree of value to all of the funding stakeholders.
U Uninterruptible Power Supply An uninterruptible power supply, also uninterruptible power source, UPS or battery/flywheel backup, is an electrical apparatus that provides emergency power to a load when the input power source, typically the utility mains, fails.
Usage Class A term from Cisco EnergyWise. It is a set of data that describes power consumption at or over a period of time, and it is associated to a device.
Utility Demand Response (UDR) Sometimes referred to as demand-side management, this refers to a broad range of strategies, tools, and
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methods used by electrical utility providers to forecast and deliver electrical supply over a given time period. This delivery capability relies heavily on business analytics and predictive capacity requirement modeling.
Utilized Capacity The total amount of power that is being used at any given time. In this book, the term describes a data set for IT and facilities assets. Utilized capacity is derived through instrumentation, and it is typically contrasted with the total nominal draw or total nameplate draw of an IT asset to determine how much an asset or architecture is over provisioned.
V Variable Frequency Drive (VFD) A system for controlling the rotational speed of an alternating current (AC) electric motor by controlling the frequency of the electrical power supplied to the motor.
Virtual Local Area Network (VLAN) A group of devices that communicate as if they were in the same broadcast domain regardless of their physical location or connection.
Virtual Machine (VM) A software implementation of a machine (computer, storage array, switch, router, appliance, and so forth) that executes programs as a physical machine does.
Virtual teams are made possible by a proliferation of fiber-optic technology, which has significantly increased the scope of off-site communication.
Virtualization A very broad term used in the IT industry to refer to the decoupling of the operation of software from a particular hardware platform. In this book, it is primarily used in the context of hardware virtualization and as a tool to be more efficient across computing and storage assets.
Voice over IP (VoIP) A family of transmission technologies for delivery of voice communications over IP networks such as the Internet or other packet-switched networks. Other terms frequently encountered and synonymous with VoIP are IP telephony, Internet telephony, voice over broadband (VoBB), broadband, telephony, and broadband phone.
Volt The practical meter-kilogram-second unit of electrical potential difference and electromotive force equal to the difference of potential between two points in a conducting wire carrying a constant current of 1 ampere when the power dissipated between these two points is equal to 1 watt and equivalent to the potential difference across a resistance of 1 ohm when 1 ampere is flowing through it.
W
Virtual Machine Mobility
Watt
The practice of transporting a running virtual machine’s images across a network to another physical host.
The absolute meter-kilogram-second unit of power equal to the work done at the rate of 1 joule per second or to the power produced by a current of 1 ampere across a potential difference of 1 volt: 1⁄746 horsepower. In terms of Electromagnetism, one watt is the rate at which work is done when one ampere (A) of current flows through an electrical potential difference of one volt (V):
Virtual Team Also known as a geographically dispersed team (GDT), it is a group of individuals who work across time, space, and organizational boundaries with links strengthened by webs of communication technology. Members of virtual teams communicate electronically, so they may never meet face-to-face.
W = VA
254
| Glossary X
Z
XML Declaration
Zero-Energy Building (ZEB)
A processing instruction that identifies the document as being XML. All XML documents should begin with an XML declaration. An XML declaration tells a browser the version of XML that is used on a web page.
A zero-energy building (ZEB) or net-zero-energy building is a general term applied to a building’s use with zero net energy consumption and zero carbon emissions annually. Zero-energy buildings can be used autonomously from the energy grid supply because energy can be harvested on-site.
Index Note to the reader: Throughout this index boldfaced page numbers indicate primary discussions of a topic. Italicized page numbers indicate illustrations.
Numbers 1E, NightWatchman, 63, 135 3D geospatial asset inventory management, 213 6sigma Room and Rack, 24 30 percent off the top approach, 48 80/20 rule, 106
A access historian, 38 access procurement, baseline data from, 59 accounting. See also energy accounting traditional frameworks, 44–45 accuracy of data, in pilot project, 54 active policies, for influencing power consumption, 157–158 activities for project, 99 Adams, Ansel, 120 Adobe Flex, sample reports, 124 Africa, CO2 emissions factors from watts, 83 AHAM (Association of Home Appliance Manufacturers), 220 air-conditioning systems, 23 always-on culture, 88 American Power Conversion (APC), 49, 135 InfraStruXure (ISX) architecture, 206 ISX Central, 78 American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE), 220 amps, 46 analytic complexity of IT, 43 analytic framework for benchmarking filtering data across assets to build, 112–113 IT and Facilities assets, 70 IT assets only, 68 Aperture, 213 Aperture Technologies, 24 application profiling, in proof-ofconcept scoping, 67 applications, assigning asset tiers to tiers for, 219 Arch Rock, 23 ASHRAE (American Society of Heating, Refrigerating, and Air Conditioning Engineers), 220 Asset Criticality Tiering, 104 asset/inventory lists, 111 for benchmarking, 45, 45 in cost calculations, 14 origin of power data, 55
asset management 3D geospatial, 213 skill set requirements, 107 asset telemetry, 24 assets. See also IT assets access across facilities and IT departments, 103 assigning tiers to application tiers, 219 energy allocation by type, 55, 55 framework for benchmark, 72 noncompliant, 87 scheduling and reservations, 199 Association of Home Appliance Manufacturers (AHAM), 220 associations, 212 Assumptions attribute, for benchmarking framework, 71 assumptions, documenting, 71 audit data, 150–151 Australia, carbon taxation, 83 Available CRAC Capacity statistic, 81 Available Electrical Capacity statistic, 80
B backward compatibility, 85 BACnet protocol, 133 Ban Ki-moon, 8 baseline data from Facilities department, 57–58 monitored data and, 155–156 Bates, Tony, 204 bell curve for power, 46 benchmark data presentation, 86–95 comparative models, 89–92 context, 86–88 sharing vision and goals, 93–95 benchmarking analytic framework for IT assets, 68 data, 213 data sources, 56–60 framework, 71–73 instrumentation, 61–63 in pilot project framework, 99–100 roll-up of assets, 73 and scalable framework, 103 scope of considerations, 43–50 data access, 50–63 data sets access, 45–50 skill set requirements, 106–107 structure of data, 64–73 Benchmarking phase of program, 65 bill-back model, 16 for electrical use, 44–45
Billing Period attribute, for facilities operation, 89 billing systems assumptions in pilot project, 54 Building-Occupancy, 58 Bloom Energy, 29 BMSs. See Business Management Systems (BMSs) Bramfitt, Mark, 218 branch circuit monitors, 62 break-even point, 198 British Petroleum, on fuel source for electrical generation, 30 British thermal units (Btu), 49, 69 broadcast domain, in networking, 174 Broer, Andy, 60 bubble charts, 55–56, 56 building management industry, Internet protocols use, 40 Building Management Systems (BMS), 146, 171, 172, 191 data collection, 134–135 instrumentation options from, 62 building mediation, 40, 41 Building-Occupancy billing, 58 Building Readings attribute, for facilities operation, 89 buildings aligning metrics to, 64 alignment matrix of business operations to, 73 electrical use, 57 energy intensity, 16 and IT networks, 40 as part of operational alignment, 72–73 business case in pilot project framework, 100–101 summarization of high-level important costs, 90–91 business case scoping, vs. civic planning, 205 business context, energy breakdowns by, 166, 166 business functions, strategic implications, 110 Business Growth attribute, for benchmarking framework, 71 Business Management Systems (BMSs), 133 policies based on, 183 business operations, alignment matrix to buildings, 73 Business-Unit Billing attribute, for facilities operation, 89 businesses, as smart load, 140
256
| C-Bus protocol • data historian C C-Bus protocol, 133 call center, electrical cost allocation for, 44 CALO (Customer Advocacy Lab Operations), 184, 195–198 cap and trade system, 27, 84, 162 capacity, accessing, 18 capacity alerts, electric consumption change based on, 37 capacity management, 27 baseline data from, 59–60, 60 capacity planning, 59 team template, 61 Carbon attribute, for benchmarking framework, 71 carbon costs, 26 carbon-crying game, avoiding, 51 carbon dioxide emissions. See also emissions from IT electrical use, 2–3, 3 carbon dioxide equivalencies, 38, 49–50 report conversion to, 163 carbon footprint, 57 carbon, in data structure, 64 carbon intensity of fuel source, 27, 28 carbon offsets, 32 Cassatt, 211 categorization, 149–156 category of entity, 130 CDN (Cisco Developer Network), 40 centralized programs, 110, 192–193 centralized scripting, for policy implementation, 182 certifications, 79 CFD (computational fluid dynamics) modeling, 49 Chambers, John, 204 checkpoints, for energy management services development, 103 China and green house gas emissions, 161 power supply, 2 Cisco Customer Advocacy Lab Operations (CALO), 184, 195–198 Green task force, 204 Internet Business Solutions Group (IBSG), 214 Product Efficiency Calculator, 215 TelePresence technology, 204 varying use patterns, 174 Workplace Resources (WPR) group, 186 Cisco Borderless Networks, 173 Cisco Building Mediator, 40, 41 Cisco Developer Network (CDN), 40 Cisco Efficiency Assurance Program, 189 website, 69
Cisco EnergyWise, 22, 85, 126, 134, 173, 220 calculators for reporting and management systems, 164 classifying with, 178 data collection, 134 development, 120, 147 domains, 138 goals, 35–36 IOS Deployment Guide, 126 for network action as policy control, 157 Orchestrator, 135, 169 power-state levels in, 129 release, 50 smart load creation, 140 software development kit, 40 storage of time-of-day events, 182 Cisco IP backbone, 36 Cisco Medianet, 173 Cisco Motion, 173 Cisco TrustSec, 173 CiscoWorks LA Management Solution (LMS), 135, 169 civic planning, vs. business case scoping, 205 classes of data, 127 classifications, applying to devices, 178–179 Clean Air Act (U.S.), 162 clients, communications with, 215 Climate Savers, 220 cloud computing, 20 “eco-ethics” of provider services, 212 and energy management, 210–212 coal, 9, 29 costs, 27 energy production from, 10–11, 11 coal-fired packets, avoiding, 28 collaboration, in virtual team model, 108 collaborative infrastructure models (CIMs), 215 collaborative success, 94 collection of data, choosing for, 126–133 communications, 206 with clients and employees, 215 importance, 109 for pilot domain results, 142–143 of project plan, 111 skill set requirements, 107 with stakeholders, 105 comparative models, for benchmark data presentation, 89–92 competition, to reduce power usage, 157 computational fluid dynamics (CFD) modeling, 49 Computer Associates, 211 computer room air conditioning (CRAC) unit, 49, 50 energy information, 78
Computing attribute, for IT operational model, 92 Consolidated Appropriations Act (U.S.), 160 consumer types, hierarchy of, 177 cooling burden factor, 49, 80 Cooling Capacities attribute, for benchmarking framework, 71 cooling costs, 23 cooling data, 49 Corporate Accounting, baseline data from, 57 corporate-level capacity planning, attributes, 60 corporate rates, vs. regional averages, 14 corporate social responsibility (CSR) function, 6 cost allocations, 16–17 cost per kilowatt hours, 80 cost reduction, with resource management, 1 cost-sharing model, 4 costs converting from watts to, 83 as priority, 79 of watts, 64 CRAC. See computer room air conditioning (CRAC) unit cube farms, electrical cost allocation for, 44 cultural fit, for project manager, 98 Customer Advocacy Lab Operations (CALO), 184, 195–198 Cyber Switching, 135
D data
characteristics for acceptable, 127 choosing for collection, 126–133 monitored, and baseline, 155–156 quality of, 77–79 Data Center consumer type, 178 data centers breakdown of energy requirements, 20 costs, 24 efficiency modeling, 214–215 energy management, 212–215 energy use, 19, 19 environmental control technology, 4 vs. lab environment’s electrical consumption, 46 load variances and, 23 mission-critical, 56 shutdown in, 63 UPS and air conditioning design, 210 watts per square foot use, 44 zoning, 211 data framework, top-level, 52–53 data gathering, 133–135 data historian, 38
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data models • European Union 257
data models, translating, 83–86 data network, mapping to physical electrical distribution system, 176 database finding, 76–77 integration, 199 schema, 127 setup, 123–126 skill set requirements, 108 decommissioning assets, 87 delivery dates, setting, 104 Dell, reporting capabilities of server platforms, 63 deployment, phases, 54 designed availability, 85 directors, PowerPoint for communications with, 76 disruption, minimizing, 110 distributed computing, energy use, 19–21 distributed database, network as, 126 Distributed Management Task Force (DMTF), 124 documenting energy management approach, 51 domain. See also energy domains energy management technologies specific to, 85 members in Cisco EnergyWise, 138 Domain Name Service (DNS), 141 downtime, zero tolerance for, 60 Duke Energy, 37 dynamic policies, for energy domain administration, 180
E E-Mon protocol, 62 “eco-ethics”, of cloud provider for services, 212 EcoBoard, 204 economic perspective, on electrical generation, 9 efficiency modeling, 214–215 electrical consumption change based on triggers, 37 costs by region, 14 data centers vs. lab environment, 46 implementing policies, 156–158 electrical grids, 2 IP-enabling, 37 smart, 37–39 electricity adverse environmental impact, 204 benchmark data creation, 47–48 costs across enterprise, 57 distribution attributes managed for, 25 mapping data network to system, 176
loads, and decision to move computing, 87 overestimating or averaging requirements, 77 reporting inaccuracies from software, in pilot project, 55 U.S. generation, 10 EMC, Information Lifecycle Management (ILM), 219 emerging nations, energy management, 162 emerging technologies, 21–24, 126 green programs, 204–205 Emerson Energy Logic, 214 Emerson Network Power, 213 emissions from electrical consumption, 17 green house gas, 159–160, 163 from IT electrical use, 2–3, 3 regulation, 27 sources in U.S., 31 employees, communications with, 215 endpoints in Cisco EnergyWise, 138 handling, 176–178 energy. See also electricity historical data resources, 8 sources, 9–11, 27–28 from coal, 10–11 world electrical production by, 10 use, 11–13, 12 in digital age, 17, 17–24 and economic progress, 1 flat-line, 87 and GDP, 15 by IT, 43 visibility, 216–217 energy accounting, 13–17 skill set requirements, 106 energy allocation by asset type, 55, 55 by platform, 55–56, 56 energy as a service (EaaS), 94 energy audit data, 151 energy broadcast domain, 175 energy consumers, classifying, 176–178 energy costs, calculating, 13–15 energy data, gathering, 76 energy domain administration, 169 local and global activities, 175–176 organizing, 171–179 physical administration, 174–175 policies, 179–182 energy domains, 103–104, 136–140, 147 breakdown, 164–166 building partitioned as collection, 148 construction and administration, 199 hierarchical network, 172 metered, 165
natural structure, 136–137 for pilot project, 141–142 report by role, 165 as smart loads, 139–140 energy footprint, control of, 32 energy intensity, 15–16 energy management assessing, 1 and cloud computing, 210–212 current state, 25–32, 117–118 current technology adoption, 26–27 for data centers, 212–215 domain specific technologies, 85 at enterprise IT scale, 60 external programs, 217–218 future of, 32–40 scope of, 26 technologies, 194 adoption, 26–27 trends, 39–40 energy markets prices, 28 volatility in, 2 energy monitoring, granular, 44 energy profiles, 46 energy reduction, organizing efforts, 186 EnergyConsumer type, 177 EnergyWise. See Cisco EnergyWise enforced policies, vs. suggested, for energy domain administration, 180–182 enterprise IT scale, energy management at, 60 enterprise management applications, 22 entity class, 128, 129–131 entity power usage class, 131–133 Environmental consumer type, 178 environmental technologist, 205 equipment, nominal power draw vs. vendor’s published requirements, 47–48 Eskom (South Africa state electric utility), 162 estimation, 77 Europe historical data resources, 8 non-OECD, CO2 emissions factors from watts, 83 power supply, 2 European Codes of Conduct for ICT, 8 European Commission, Joint Research Centre Institute for Energy, 8 European Union, 29 CO2 emissions factors from watts, 83 Emission Trading System (EUETS), 162 financial incentives for energy efficiency, 195 historical costs, 14 regulations for reporting greenhouse gas emissions, 161
258
| Excel spreadsheets • International Organization for Standardization (ISO) Excel spreadsheets, 76 execution of project, 109 executive-level context, 86 executives PowerPoint for communications with, 76 priorities, 79–80 support, 95, 200 existing programs, collaborating with, 6–7
F face-to-face meeting, vs. video, 88 Facilities Asset Profiles, 104 Facilities department assets across, 103 audit data, 150 baseline data from, 57–58 bill-back model, 44 energy information, 78 operations, 191 analytic models for, 89 data model for, 82–83 power and cooling data from, 61–62 tracking data, 218 facilities industry tools, 7 facilities management, skill set requirements, 107 facilities overhead, 14 Facilities professionals, as team members, 106 facilities team, 110–111 FCAPS model, 118, 176 FCAPS+E model, 118 feedback, 168 filtering data, across assets to build analytic models, 112–113 Finance departments, baseline data from, 58–59 financial accounting, skill set requirements, 106 financial data, energy information from, 78 financial modeling, skill set requirements, 107 Fixed Consumer type, 177 fixed costs, electricity viewed as, 13 flat-line energy use, 87 flex days, 38 Floor Space attribute, for facilities operation, 89 Ford, Henry, 114 fossil fuels, dependence on, 9 framework. See also analytic framework for benchmarking for benchmarking, 71–73 for pilot project, 99–102 fuel cells, 38 fuel source effectiveness, 14 funding managed service models, 188 programmatic models, 186–188
sustainability and, 185–188 traditional IT service models, 187–188
G Gartner 2007, 17 geospatial power-allocation systems, for electrical cost allocation, 44 Ghose, Tirth, 34, 120 global CO2 emissions factors, from watts, 83 global energy market, volatility in, 21 global energy use, 11 by use case, 12 global greenhouse gas, from IT electrical use, 17, 18 global resource consumption, data analysis, 2 global warming debate, 51 goals of project, 98 sharing, 93–95 Google, 43, 136, 194 photovoltaic (PV) installation, 38 SketchUp, 215 Google Earth, 28 Gore, Al, Our Choice: A Plan to Save the Climate Crisis, 9 government mandates for reports, 160–163 effectiveness, 162–163 government subsidies, 29 governmental reporting, 38 granular energy monitoring, 44 Green Building Extensible Markup Language (gbXML) protocol, 111 Green Data Center Model Calculator, 214–215 Green Grid, 69, 220 metrics of power usage effectiveness (PUE), 14, 28 green house gas emissions, 159–160 report conversion to equivalencies, 163 Green IT, 36 Green programs, 1, 6, 7, 84–85, 88, 94 emerging technologies, 204–205 labeling, 4 path for quantifying and incrementing efforts, 88 project implications, 97 scope of program, 185 vs. sustainability, 26 growth constraints, for corporation, 18 growth percentages, aligning, 53, 54 growth projections, in pilot project, 54 guiding statement, for project phases, 44
H hard instrumentation, 62–63 hardware energy information for, 34 for pilot project, 122–123 power usage information for platform, 33 heat removal requirements, 104 heliostatic solar plants, 29, 31 Herzberg, Frederick, 105 hierarchical structure, maintaining, 136 historical data reports, 166–168 Hospitality consumer type, 178 hot spots, 23 HP, 194 business Technology Optimization Software, 135 ProCurve, 135 reporting capabilities of server platforms, 63 Systems Insight Manager, 22, 63 HVAC, measuring, 69 hydroelectric power, 28
I IBM, 194 PowerExecutive, 63 reporting capabilities of server platforms, 63 Systems Director, 22 Tivoli, 22, 134, 135, 169 idle IT assets, 104 IETF (Internet Engineering Task Force), 220 ILM (Information Lifecycle Management), 21 importance ratings, 151, 153–154 examples, 155 incentives for energy efficiency, 216, 218 India, power supply, 2 Information Lifecycle Management (ILM), 21 information review, 159–160 information technologies. See also IT ... efficiency at scale, 16 for energy management, 2 for improved resource management, 204 reduction in total energy use related to, 16 infrastructure, attributes managed for, 25 instrumentation, 23 for benchmarking, 61–63 hard, 62–63 soft, 63 tapping into existing, 61–62 for water management, 208–209 International Organization for Standardization (ISO), 118
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Internet Domain Name Service (DNS) • nuclear energy 259
Internet Domain Name Service (DNS), 141 Internet Engineering Task Force (IETF), 220 Internet protocols (IP) building management industry use of, 40 load categories manageable with, 33 for power consumption report, 34 inventory, performing, 149–156 investment, price-point drivers for, 205 IP. See Internet protocols (IP) IP-address level, close-to-real-time information on energy use, 60 IP based technology, 171 IP network, information from, 122 iPhone, infrastructure to support, 21 Ipsen, Laura, 204 IT and Facilities assets, as scope, 68–69 IT architecture, skill set requirements, 107 IT Asset Utilization statistic, 81 IT assets aligning with real estate, 92–93 information on energy use, 78 as scope, 67–68 IT departments analysis of sustainability initiatives, 5 assets across, 103 baseline data from, 57 bringing building and IT networks together, 40 context for, 86 energy use, 17 growth in energy use, 4 priorities of operations, 80–81 tracking data, 218 IT management, skill set requirements, 108 IT networks, as smart load, 140 IT operations analytic models for, 92 as focus, 189, 191 IT platforms, consolidating, 87 IT service models, traditional, 187–188 IT teams, 111
J Japan historical data resources, 8 power supply, 2
K keywords (tags), 151, 154, 178 for entity, 131 examples, 155 kilovolt-ampere (kVA), vs. kilowatt (kW), 14
kilowatt (kW), vs. kilovolt-ampere (kVA), 14 knowledge-sharing, in virtual team model, 7 kW-per-square-foot, 44 Kyoto Protocol, 161
L Lab consumer type, 177 lab environment, vs. data center’s electrical consumption, 46 Laherty, Matt, 34, 208–209 LAMP (Linux, Apache, MySQL, and PHP), 124–125 Latin America, CO2 emissions factors from watts, 83 Leadership in Energy and Environmental Design (LEED), 79, 218 leadership qualities, for pilot teams, 119–120 Lee Jun Fan, 215 Level of Effort, for project, 99 Lewis-and-Clarking, 117 Liebert SiteScan Web, 78 live reports, 166–168 load level, efficiency, 14 load variances, data center to accommodate, 23 local generation of electricity, 38–39 logistical management, 109 LonWorks protocol, 133
M macro-asset profiling, 200 man-hours, 108 managed service models, 188 management-by-objective (MBO) goal, 198–199 management domain, 137 management, in pilot project framework, 102 manual activities, for influencing power consumption, 157 Marcoux, Paul, 210 marketing, 215 skill set requirements, 107 massively scalable data centers (MSDCs), 12 master document, for project, 76 McKinsey & Company, 22 measuring and consumption change, 126–127 HVAC, 69 mercury, from coal, 11 metering, 137, 147 and energy domain, 141 methodology of project, 99, 103–104, 199–200 MFC. See microbial fuel cell (MFC) micro-grids, 32, 39 microbial fuel cell (MFC), 29
Microsoft, 194 Middle East, CO2 emissions factors from watts, 83 milestones in project, 99, 111–114 mining data, 76 mission statement, 114 defining for pilot project, 120–122 mobile computing loads, 212 distributed, 211 Mobile consumer type, 177 mobility, of computing and storage, 22 Modbus protocol, 62, 133 modular data model, 64 modularity, 206 monetary investment, vs. public opinion, 3 monitored data, and baseline, 155–156 Moore’s law, 16
N nameplate power draw vs. nominal power draw, 47–48, 48 normalizing, 78 National Geographic, 208 National Renewable Energy Laboratory (NREL), 32 natural resource management, 205 Nayeem, Sheikh, 186 Nelson, Dean, 88 NetApp, 218 network audit data, 150 hierarchical campus, 173 hierarchy with energy domains, 172 infrastructure as physical topology vs. cloud of services, 172 storing policies in, 176 network-based configurations, for policy implementation, 182 Network consumer type, 178 Network Data Center Services (NDCS) teams, 81 Network Management Systems (NMSs), 133, 146 data collection, 135 policies based on, 183 Networking attribute, for IT operational model, 92 networking consumption, 2 New York Times, 126 nominal power draw, vs. vendor’s published requirements, 47–48 noncompliant assets, 87 Nonstandard assets attribute, for IT operational model, 92 Not Currently Productive Electrical attribute, 104 NREL. See National Renewable Energy Laboratory (NREL) nuclear energy, 28
260
| Obama • remote terminal units O Obama, Barack, 3 oBIX protocol, 133 occupancy-based systems, for electrical cost allocation, 44 offices, as smart load, 140 offsets, 31–32 oil, 9 Open Systems Interconnection (OSI) model, 150–151 Open Virtualization Format (OVF), 124–125 OpenEco, 8 OpenView, 22 operating cost of computing system, 83 estimation, 14 operational reports, 166–168 operations, matrix aligned to, 72–73 operations stack, program integrated into, 188, 190 organization, 114–115 organizational impact, scope of, 53, 53 organizing data, 75–83 ensuring data quality, 77–79 finding database, 76–77 prioritizing data, 79–83 OSIsoft, 76 Pi Server, 62 OVF (Open Virtualization Format), 124–125 Ownership of project, 99
P Pacific Gas and Electric Company (PG&E), 32, 37 Pacific Northwest, hydroelectric power, 28 Pacific Northwest National Laboratory (PNNL), 126 Parello, John, 34, 36 partitioning, 147–148 passion, 94 passive activities, for influencing power consumption, 157 PC consumer type, 177 PC systems, data collection, 135 PDUs. See power distribution units (PDUs) performance contracting models, 186–187 permission, for data access, 51–56 perspectives, short- vs. long-term, 26 petroleum-based energy, government subsidies, 29 philosophy of pilot project, 120–122 photovoltaic (PV) technologies, 38 pilot project assumptions to clarify, 54–55 basic framework, 99–102 communication of pilot results, 142–143
data gathering, 133–135 database setup, 123–126 energy domains, 141–142 mission and philosophy, 120–122 review before rollout, 147 root system creation, 122–135 hardware requirements, 122–123 team selection for, 119–120 Pilot project phase of program, 66 planning, capacity, 59 platform, energy allocation by, 55–56, 56 PM. See project manager (PM) PNNL (Pacific Northwest National Laboratory), 126 policies for energy domain administration, 179–182 examples for fulfillment tracking, 183 implementing, 182–183 for power consumption management, 156–158, 176 political action, potential changes to electrical costs, 85 power. See also electricity power and cooling data, from Facilities department, 61–62 Power Assure, 211 Power Capacities attribute, for benchmarking framework, 71 power distribution units (PDUs), 34, 35 Power over Ethernet (PoE) applications, 34, 35, 35 power-state levels, 128 in Cisco EnergyWise, 129 power usage effectiveness (PUE) ratio, 69 power usage information, for hardware platform, 33 PowerPoint, 76 modular slide deck, 51 preparation, 203–215 presentation of benchmark data, 86–95 comparative models, 89–92 context, 86–88 sharing vision and goals, 93–95 on executive level, 95 of value proposition, 51–55 price-point drivers, for investment, 205 priorities, 105 hierarchy of, 7 for IT operations, 80–81 product efficiency calculators, 48 production data center environments, vs. lab teams, as first, 93 Production Electrical assets, 103 production plan, creating, 145–146 production power, vs. total operative power capacity, 104
production rollout, 145 performing inventory and categorization, 149–156 reviewing pilot project before, 147 steps, 146 productivity, for global energy market, 21 Program phase, 66 program placement, 188–191 program structure centralized, 192–193 distributed, 193–194 scalability, 194–200 programmatic taxonomy, 65–66 project management drafting framework, 98–105 execution, 109 guiding statement for phases, 44 methodology, 103–104 milestones, 111–114 timelines for deliverables, 104–105 tools, 114 visibility, 109 project manager (PM), 97–98 project master document, 76 project reviews, establishing parameters for, 98 proof-of-concept (POC), 105 deployment, 53 as program phase, 65 protocols, 133 public opinion vs. monetary investment, 3 polls on global energy concerns, 6 Purdue University, Vulcan project, 31
Q Quality Attributes, 188 quality of data, 77–79
R Rackwise, 24, 213 Rasmussen, Neil, 206 real estate function, 191 aligning IT assets with, 92–93 baseline data from, 58 energy costs managed from, 13 real estate management, skill set requirements, 108 rebates, 31–32 recognition, of individual efforts, 94 RECs (renewable energy credits), 32 regional averages, vs. corporate rates, 14 regional fuel mix, 57 regions, power costs by, 14 Reid, Thomas, 203 reliability of energy, 27 remote monitoring service models, 188 remote terminal units, 62
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renewable energy • Total floor area statistic 261
renewable energy, 28–32 renewable energy credits (RECs), 32 reports automated capabilities, 199 conversion to GHG and CO2 equivalencies, 163 energy domain breakdown, 164–166, 165 government mandates, 160–163 information review, 159–160 mock-ups, 168 for time-based sets, 166–168 tools for pilot project, 123 reservation-type devices, policies for, 179 reservations assets, 199 Reserved consumer type, 177 resilience of operation, 85 resource management, 219–221 cost reduction with, 1 tiering, 219–220 resources, aggregating, 109–111 responsibilities, of project manager, 98 Ricci, Ron, 204 Richards-Zeta (RZ), 40 risk, 85–86 capacity planning team aversion to, 60 from electricity, amps and, 46 in pilot project framework, 101 skill set requirements for analysis, 107 roles, 151–153 of entity, 130 examples, 155 Rolfsen, Rob, 186 rollout. See production rollout RS-485 connection, 62
S savings, demonstrating potential, 96 SCADA protocol, 205 scalability of program, 194–200 methodology, 199–200 vision and execution, 198–199 scheduling assets, 199 scope of benchmark program, 64–69 of energy management, 26 of program, human resource requirements, 67 scope creep, 53 security, for network, 172 selecting teams, 119–120 sell back to grid, 37 sensor networks, 23–24 cooling costs and, 24 Sentilla, 211 server consolidation, 87 Service phase of program, 66 services profiling, in proof-of-concept scoping, 67
shutdown, in data center environments, 63 shutting equipment off, savings from, 47 skill sets, 106–108, 191 professionals to fil gaps, 69 requirements, 106–107 smart grid, 37–39 smart loads, 32, 33–36, 137–139 energy domains as, 139–140 smart power distribution units, 62–63, 135 smart power rail, 34 Smith, Andy, 186 SMPS (switched-mode power supply), actual power draw, 48 SNMP protocol, 133, 157 social groups, energy domains grouped by, 148, 149 soft instrumentation, 63 software as service (SaaS), 20 solar concentrating plants, 29, 31 solar power, 29 SolarWinds Orion, 134, 135, 168 South Africa Energy Efficiency Accord, 162 spin-down features, of servers, 63 spreadsheets, 76 creating manually, 134 SQL (Structured Query Language) database, 75, 76 stakeholders, 71 communications with, 105 separating from functional team, 105 standardization, 110, 206, 220 standby, power consumption of, 87 static policies, for energy domain administration, 179–180 step-down transformers, 46 Storage attribute, for IT operational model, 92 storage infrastructure and energy requirements, 21 trends, 20 strategic value, prioritizing and quantifying, 79 structure of data, for benchmarking, 64–73 submeters, 44, 62 success project metrics, 99 structure for, 114–115 suggested policies, vs. enforced, for energy domain administration, 180–182 Sullivan, Dick, 219 sun, 9 Sun Microsystems, 8 reporting capabilities of server platforms, 63 supervisory control and data acquisition (SCADA) networks, 2
sustainability. See also Green programs funding and, 185–188 vs. Green program, 26 program placement, 188–191 program structure choice, 192–194 switched-mode power supply (SMPS), actual power draw, 48 SynapSense, 23, 214 systems, building data into existing, 77 systems integration, skill set requirements, 108
T tags (keywords), 151, 154 examples, 155 teams building, 105–114 facilities, 110–111 methodology to build, 200 PowerPoint for communications with, 76 selection, 119–120 virtual, 108–109 technologies, emerging, 21–24, 126 telemetry, 23 temperature management, 214 temperature sensor networks (TSN), 211–212, 214 template, for capacity planning, 61 thermal imaging, 24 thermal map for environment, 214 Thermal Profiles, 104 throw, 49 tiering, for resource management, 219–220 Time attribute, for benchmarking framework, 71 time-based sets, reports for, 166–168 time, in data structure, 64 time-of-day costs, and consumption profiles, 37 Time-of-Day Costs attribute, for facilities operation, 89 timelines for project, 99 Tivoli, 22, 134, 135, 169 Tolstoy, Leo Nikolayevich, 185 Total Buildings count, 80 Total Buildings with Data Center Space statistic, 82 Total Buildings with Lab Space statistic, 82 Total Capacities attribute, for facilities operation, 89 Total CO2e statistic, 80, 83 Total Costs statistic, 80, 81 Total CRAC Capacity statistic, 80–81 Total CRAC statistic, 80, 82 Total Data Centers statistic, 81 Total Electrical Capacity statistic, 80 Total Electrical statistic, 79 Total Facilities Asset Count, 81 Total floor area statistic, 80, 82
262
| Total HVAC statistic • zoning Total HVAC statistic, 79–80 Total IT Asset Count, 81 Total IT Costs statistic, 82 Total IT Electrical statistic, 79 Total IT Load statistic, 82 Total Lab statistic, 81 total operative power capacity, vs. production power, 104 Total UPS statistic, 82 trading floors, electrical cost allocation for, 44 traditional IT service models, 187–188 transformers, 46 Transient Consumer type, 177 truck roll, 25 TSN (temperature sensor networks), 211–212, 214 turbine technologies, 38
U UDR (utility demand response), 25, 37 Uninterruptible Power Supply (UPS) energy information, 78 legacy, 23 United Kingdom, carbon taxation, 83 United Nations Climate Change Conference (COP15), 161 United States cap and trade system, 27, 84, 162 CO2 emissions factors from watts, 83 electrical grid investment, 3 emissions sources, 31 energy generation, distribution and transmission estimates, 12 EPA power profiler, 18, 47 federal agencies sustainability measures, 161 financial incentives for energy efficiency, 195 historical costs, 14 historical data resources, 8 power supply, 2 United States Green Building Council (USGBC), 218 U.S. Department of Energy, on Executive Order (E.O.) 13514, 161 U.S. Environmental Protection Agency (EPA), 14 calculators and formulas, 163, 163 eGRID site, 28, 49
regulations for reporting greenhouse gas emissions, 160–161 U.S. Geological Survey, estimated water use in U.S., 208 unity, 15 upgrade costs, 26–27 Urquhart, James, 212 usage class, 128, 131–133 user interfaces, mock-ups, 168 user requirements, gathering, 103 utility demand response (UDR), 25, 37 utility grids, as smart load, 140 utility providers, alerts on full-capacity risk, 38
V valuation matrix, for executive support, 95 value assessment organizing data, 75–83 ensuring data quality, 77–79 finding database, 76–77 prioritizing data, 79–83 translating data models, 83–86 formulaic approaches, 83–84 qualitative approaches, 84–86 value proposition, 94 adding non-IT assets to benchmarking to increase, 68 aligned capacity analysis for presenting, 53, 54 components, 52 example, 51 presenting, 51–55 vampire power, 104 Vblocks, 23 vector scale, for entity usage, 131 Verdiem Surveyor, 135 video collaboration, vs. face-to-face, 88 virtual appliance resources, 124 virtual machine motion, 210–212 virtual team, 108–109 collaborative model, 7 VirtualBox, 123, 124 virtualization, 20, 87 of computing and storage, 22 visibility of energy use, 216–217 vision, 198–199 sharing, 93–95
Vision phase of program, 65 vision statement, 114 VM provisioning, tying energy data to, 211 VMware, 123, 124, 211 VMotion, 23 Voice over Internet Protocol (VoIP), 146 voltage, regional requirements, 47 volts, 46
W Walk-In consumer type, 177 water management, 205–209 wattage requirements, 47 watts, 46, 64 converting Btus to, 69 converting to cost, 83 for cooling data, 49 global CO2 emissions factors from, 83 watts per hour, 46 web resources Cisco Efficiency Assurance Program, 69 eGRID site, 28, 49 EPA power profiler, 18, 47 web server, 123 wide area network, energy management for, 34 Wikipedia, 46 wind energy, 9 workloads, defining, 220 Workstation consumer type, 178 world electrical production, by source, 10 WTI, 135
X Xen, 124
Y Yahoo!, 136
Z zero-energy building (ZEB), 32 zero tolerance, for downtime, 60 zoning, for data center, 211
